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Contact us if you need a Mobile Payments integration Modern mobile payment systems make this task easier, but before choosing one you should understand how they all work. In our new article, we’ve explained how mobile payments are organized and which things to consider while integrating them with your app. Check it out and start getting an edge with the right mobile payment solution. Introduction of Google Wallet (now is Google Pay) inspired a gradual decline of traditional heavy leather wallets. It's no longer OK for mobile apps to use one tunnel for card-based transactions. To reach a wider audience of progressive users, one should also accept other payment types like digital wallets, Automated Clearing House (ACH) payments, and cryptocurrencies. However, integration of mobile payments functionality into a mobile app is more than just adding a new app screen and writing a bunch of code. Read our article to find out what things to consider while adding mobile payments to your app. Mobile payment gateway A mobile payment gateway is a front-end technology that authorizes a transfer of funds between a user’s payment portal (mobile phone) and the merchant acquiring bank. One can think of it in the same way as of a traditional Point of Sale (POS) terminal. Source: squareup.com At checkout, the gateway transfers the cardholder information to the issuing bank to verify the request. The data is further handled by a payment processor at which one has a merchant account, although some processors have their own gateways. At this stage, the bank will either approve or reject the payment with the corresponding message appearing on the end user’s mobile screen. The payment gateway is actually an API you integrate to make a request for charging a customer's card. Most reputable payment platforms provide an API that works with the backend language of your mobile app. Using this API, the app can talk to the payment platform. Typically, API integration services can complete the integration within a few working days. The data traffic that goes through a gateway is transferred privately and always enciphered. If the payment information was transmitted right to the processor (without a gateway stage) it could be easily interpreted. This would allow an intruder to make fraudulent transactions. Integration strategy The integration strategy depends on the types of goods being offered to the customers. Typically, there are two options you can have: Virtual goods (in-app purchases). Both Apple and Google take a 30 percent off any transaction that is made within your mobile app for in-app purchases. For this reason, both OSes do not allow using any third-party payment services and provide the developers with their specialized StoreKIt framework and In-App Billing API for iOS and Android respectively. The purchases are made in AppStore or Google Play via Apple or Gmail accounts that users are already supposed to have. Source: developer.apple.com/documentation/storekit Physical goods and services. When it comes to the goods and services outside of the app, both Apple and Google recommend using third-party mobile payment gateway providers. However, a platform will charge a percentage of the transaction as a fee. The most common figure is 2.9 percent. How to choose a payment solution According to the annual Mobile Payments & Fraud report, merchants that provide mobile payment capabilities in their apps offer a wide range of payment methods. However, there is a gradual shift from standard credit and debit cards to PayPal, mobile wallets, ACH or bank transfer payments, prepaid cards and cryptocurrencies. The top two consideration when choosing a payment method are: How well it integrates with your payment platform and bank account. In fact, most of the well-known payment platforms support the popular mobile payment solutions like Apple Pay, Google Pay, PayPal, Samsung Pay as well as ACH and traditional swipe cards. For cryptocurrency adopters, there will be probably a need to turn to specialized payment gateways. Yet, such payment giants as Paypal (through Braintree) and Shopify do allow their customers to pay with bitcoin, while Stripe has officially stopped its support. How secure payment data is. “The biggest fear of corporates and consumers is that transactions will not be processed properly, that their bank access details might be compromised and that their data and therefore their money may be stolen. This is why the focus on data and data security is the key to the future," Chris Skinner, Digital Bank: Strategies to launch or become a digital bank. Today, mobile payment providers have a set of security measures to stick to. Most of them never store raw cardholder information without tokenizing or encrypting it. Tokenization is a process of substituting sensitive information like the PAN (primary account number) with an algorithmically generated non-sensitive counterpart called a token to prevent credit card fraud. It means that during the payment processing the actual card data is never exposed. Tokenization is mathematically irreversible unless you get access to the original key used to generate a token. Even if the system is hacked, all the fraudster will see is a bunch of randomized devalued symbols. Source: https://developer.samsung.com Encryption transforms the data into a form unreadable by anyone without a secret decryption key. Its purpose is to ensure privacy by keeping the information hidden from anyone for whom it is not intended, even those who can see the encrypted data. Both practices decrease the number of systems allowed to see the customer’s data, thus reducing the scope of PCI Compliance. However, neither Apple Pay nor Google Pay does adhere to the standard. Therefore, they need to be integrated with the PCI-compliant payment platform, like PayPal’s Braintree or Stripe and the issuing bank must be PCI compliant. Final thoughts Integrating payments to a mobile app may seem not a big thing as reputable payment systems provide well-built APIs. Yet, being aware of the industry nuances can help to avoid unwanted risks related to the security considerations and technology deployment.

Source: https://actioncoach.co.za ‘In software systems it is often the early bird that makes the worm.’ Alan Perlis In September of 2016, users downloaded approx 130 billion apps from the App Store, and about 2.23% of those downloaded were financial apps. In 2018 the picture looks like that: Source: www.statista.com Financial apps didn’t gain popularity on charm alone. Finances, at last, became a manageable task you can resolve from any place and using different devices. In the mobile section, you can see advisors, budget-builders, online-banking and many more. However, here we’re interested mostly in web applications that stand behind every modern company in the world.  But even though some companies are making good dough, the inside is dying out. Integration is the easy way to prevent your business from fading away and increase software functionality. ‘Grow fast or die slow.’ Silicon Valley series Cooperation expands your software. And this in turn gradually improves the quality of the services you offer, and make them relevant for the next decades. And before we start, the key questions of the article are: What is financial software, its definition, and types Accounting software Insurance software Banking software Trade and stock exchange software Why integrate these virtual creatures? Monsters under the covers Small business vs Large enterprise   Intro to the financial software Financial software is designed to automate, assist and store financial information, whether it personal or business. Moreover, this software store, analyse, and handles management and processing of financial transactions and records. It may be a standalone software or a part of a financial information system (IS). Most financial software incorporates all aspects of personal or business finance and provides numerous features, including: Basic financial data management Financial transactions Budgeting Account management Financial assets management   Financial software also may provide other related services, such as accounting, bookkeeping, and be integrated within other enterprise information systems. Accounting software Source: financialfuse.co.uk Accounting software automates accounting and finance-related tasks. It stores and analyzes transactions within diverse functional domains of accounting and finance. Key features: Integration with banking & insurance systems Accounts payable Accounts receivable Cash flow management Tax and compliance management Payroll management Insurance software Source: https://bancorpinsurance.com Insurance software is designed to help manage day-to-day operations and monitor the administrative side of insurance companies. Moreover, it allows clients to check their policy information, fill out forms and make online payments over the internet. Banking software Source: https://www.rcrwireless.com/ Banking software typically refers to Core Banking and trading software that is used by investment banks to access capital markets. Features of the banking software are: Commercial billing system (refinancing and some daily operations, including billing, collections/recovery, and interest rate adjustments) Making and servicing loans Opening and managing new accounts Processing cash deposits and withdrawals Processing payments and cheques CRM (Customer Relationship Management) activities Managing customers accounts Setting minimum balances, interest rates, number of withdrawals allowed etc. Maintaining records for all the bank’s transactions.   Trade and stock exchange software Source: http://cryptotimes.org Trading software helps investors improve their stock picking decisions through its fundamental analysis and advanced technical analysis. Stock market trading software is relied on by traders to pick out shares quickly. Some of the most common features include: Placing Trades Technical Analysis - (interactive charting capabilities, including both chart patterns and technical indicators) Fundamental Analysis (financial statements, analyst ratings, etc.) Programmatic Trading - advanced trading software rules out the necessity of manual clicking by developing programmatic trading systems. In addition, there’s the function of backtesting designed to see how automated trading systems would have performed in the past Paper Trading means placing faux trades. That way, traders can test out their skills and see how they would perform before committing actual capital   Why integrate? ‘Companies in every industry need to assume that a software revolution is coming.’ Marc Andreessen Well, first of all, you don’t have to reinvent the wheel. There’s no need to pay millions for the app that implements everything that has already been done. Instead of the one-shot-application, gather the best existing ones. Secondly, spreading business functions across multiple applications creates a flexible business with a choice to get the best (accounting package, CRM etc.). However, implementation of the request commonly involves several inner systems at once. This creates the necessity of a solid connection between them so that the data flow becomes much safer. Moreover, optimization of systems interaction (elimination of any discrepancies between them) decreases the overall time of development and prevents the need to start from scratch.   Moreover, integration makes the connection between supply chain management, customer relations management, and business intelligence simpler and smoother. So, instead of changing the whole application some business processes will become automated due to the “simple” integration solution. So, in order to support the effective implementation of business functions and reliable data exchange, software integration is a good choice. If you want to perfect your business, we at Belitsoft are quite experienced in integration solutions. Contact us here for a free quote and expert advice! Monsters under the covers The pitfalls concealed under the thoughtful word “integration” may change one’s mind to get the ball rolling. However, let’s get through the cover and see the truth. Data security is the most important aspect of finances. And here’s the place for the tethered goat to hide because data protection is what many companies struggle with. Careless integration may compromise it: a hacker accessing one of the systems can access them all. In this case, the integrated app is a weak spot. Moreover, integrating applications can actually create new vulnerabilities, because the figurative portals through which data flows from one system into another are the natural Achilles heel that crackers and/or your own employees can have an advantage of. When it comes to the finances, enterprise software inevitably comes up. Here, software promoters offer EAI suites that provide cross-platform, cross-language integration in addition to cooperation with many popular business apps. However, the true challenges of integration span far across business and technical issues. For example: Enterprise integration requires a change in corporate politics. Business apps mostly focus on a specific functional area, such as Customer Relationship Management, Billing, Finance, etc. As a result, many IT groups are organized in alignment with those. Once the most critical business functions incorporated into the integration solution, that well-functioning solution becomes vital. A fail here costs millions of dollars in lost orders and misrouted payments which lead to angry and never-come-back customers. Next difficulty you may probably meet is lack of control. In many cases when you want to integrate your software with others’ legacy systems and/or packaged applications. They can’t be changed just to be connected to your integration solution. This often leaves your developers nothing more than making up for deficiencies or peculiarities inside the applications and differences between them. Moreover, despite the widespread need for integration solutions, only a few standards are broadly used today (XML, XSL and Web services). In the meantime, the excitement centered around Web services has led to new fragmentation of the market, resulting in a flurry of new “extensions” and “interpretations” of the standards. Even though XML is treated as a versatile way of presentation, bringing all data exchange to it is just the same as if somebody wrote all the documentation in the world using only the Roman alphabet. It is common, but cannot be easily understood by all readers. So, in spite of the same “interpretation way” (XML), we have to meticulously eliminate the semantic differences between systems what will cost time and additional efforts.   Small business vs Large enterprise. All that different? Source: http://www.dijitalyol.com Enterprise integration software is the use of software and computer systems' architectural principles to integrate a set of enterprise computer applications. it mainly focuses on system interaction, EDI, data exchange, and distributed computing devices: The first question is why to integrate already complex software that runs behind the scenes of a huge corporation. Well, it is clear that any company, especially large and “extensive”, works well while all the elements cooperate perfectly. So frankly speaking, most of the giants that exist today have integrated their systems and live happy life serving clients and milking them as long as they need to. Now let’s put puzzle pieces together. To realize how extend the enterprise back-end itself, see the main ERP modules: What makes the enterprise integrated software so different from the “ordinary” one? Well, all the systems above are linked and operate naturally as a network. Moreover, software integration improves data flow across multiple systems by modifying the connections between them into solid links and cleans them up by means of API. Important thing is that API gets an access to the systems’ information without breaking the connection between them. These facets as a whole create secure point-to-point communication channels, what allows developers to access information responsibly without affecting the connection. The key purposes of enterprise software integration are to make the easy access to the information and turn the app into the complex versatile field with the entire business stuff on board. From now on you don’t have to use any additional tools to offset the blank spaces of the original application. Conclusion Flexibility mostly defines constant development which is the crucial aspect to be in demand on the market. While you read this article, the modern IT industry keeps changing. To survive and open up new levels one needs to move with times and learn how to cooperate. Integration is another word for the partnership where systems perfect each other and grow. And even if you don’t run a company $100M company, it doesn’t mean integration is less profitable. Integration is one of the numerous facets which make the remote functioning of giant corporations safer, easier and helps to earn money having only clouds over the head. In the end, software integration makes data exchange more efficient, reliable and secure what improves the communication between diverse enterprise applications manifold. So, if you’ve chosen the path of IT and already think through the common pitfalls that drove many successful startups to an earlier grave, start the creation from the very beginning with us!

"Business analyst helps guide businesses in improving processes, products, services, and software through data analysis. These agile workers straddle the line between IT and the business to help bridge the gap and improve efficiency."CIO Magazine Business analysis delves into understanding the domain, capturing its systems and processes, and establishing key business criteria. This serves as the foundation for detailing both functional and non-functional specifications, with the ultimate aim of proposing optimal solutions for software product development. Business analysis can make or break your financial software development. What are the risks if you bypass this phase? Avoiding Time and Budget Overruns. With a clear project vision, a business analyst helps mitigate financial and operational risks. They construct a vital timeline and budget forecast, align it with market trends, and devise a strategy to meet these goals. Minimizing Rework Risk. Unmet software needs often stem from communication gaps. Business analysts act as bridges, aligning stakeholders and developers with business goals and tasks. They convert business concepts into technical requirements, preventing misinterpretations that could necessitate revisions. What are the Responsibilities of a Business Analyst? As the fintech sector expands, the need for business analysts with expertise in both finance and technology also grows. Business analysts in this sector operate across numerous specializations, from cryptocurrency development to roles within credit card companies. Their primary function is to simplify communication by translating technical concepts into business language to improve decision-making, efficiency, and success of fintech initiatives. A fintech business analyst typically undertakes the following responsibilities: Elicits and evaluates business requirements from stakeholders to fully understand their needs. Conducts market research for industry trends and opportunities. Collaborates with stakeholders, developers, and UI/UX experts to define functional and non-functional requirements. Documents user stories, use cases, and process flows for clear communication. Works closely with the development team to meet requirements for the final product. Helps ensure quality and alignment with intended functionality during testing. Provides support during user acceptance testing and helps address any issues or concerns that arise. A business analyst can also act as a product owner, particularly in larger companies, to address operational issues and engage with clients. In this role, they collaborate with stakeholders and the development team to prioritize features, create a product roadmap, and gather feedback for product enhancements. They manage client meetings and facilitate cross-functional collaboration for timely delivery of quality products. Why Domain Experience Matters An analyst who has deep subject knowledge is usually far more effective than a more general specialist. What does it mean for a client and the project? Rapid onboarding. Thanks to deep financial domain knowledge, a business analyst can integrate into the project faster, accelerating the project kick-off and saving valuable time. Budget efficiency. Business analysts with financial domain expertise can ask in-depth questions, anticipate clients’ needs, identify and challenge assumptions, and adopt a proactive approach, leading to fewer errors and, consequently, budget savings. Future-proof products. Experts versed in the financial software market can identify key functional and design aspects. Using this knowledge, they craft standout software products that effectively cater to users' needs and stand the test of time. Benefits of Business Analysis for Financial Software Development Compliance assurance. With business analysis, the professionals meticulously craft the requirements for your software applications, ensuring compliance with all relevant laws and regulations. This includes international banking standards, anti-money laundering laws, and data privacy norms. Increased ROI through automation. An IT business analyst defines and prioritizes product functionalities, identifying tasks that can be replaced with automated to minimize mundane tasks and human errors. This could involve auto-calculating interest rates, generating financial reports, or streamlining transactions, leading to improved productivity and operational efficiency. Risk mitigation. 71% of software projects fail due to poor requirements. Business analysis can help avoid the risk of failed initiatives by using business analysis for the Proof of Concept. Business analysts prioritize the implementation of requirements offering the highest potential benefit to the customer. Additionally, financial or banking software can be designed to predict potential market changes or alert users of risky investments, aiding financial institutions to minimize losses and optimize gains. Development cost reduction. Accurate product definition and requirement prioritization eliminate the need for unnecessary changes or reworks. This is achieved through logical and systematic decision-making, where solutions are tailored and aligned to the specific needs of the business or customer. Quicker market entry. Speeding up product delivery and being first to market gives you a competitive edge. A clear roadmap outlining the transition from current to future state, combined with stakeholder consensus, can facilitate this process. Advanced security assurance. Business analysis aids in embedding advanced security features like encryption, two-factor authentication, and intrusion detection systems into the software. This approach not only safeguards against data breaches but also maintains the integrity and confidentiality of user data. Enhanced decision-making. A skilled IT business analyst — often working alongside a BI Consultant for Fintech — uses financial software analytics tools and predictive models to deliver data-driven insights about market trends and customer behavior. This aids decision-making, leading to improved bottom line, management, cost efficiency, team collaboration, sales, and project success rates. Competitive advantage through digitization. Business analysts can assist financial institutions in transitioning their services to the online domain, effectively meeting customer needs. By spotting trends in technology transformation, these professionals guide institutions in adapting to the swiftly evolving landscape. The Role of Business Analysis in Software Development Discovery Phase with Clear Project Requirements and Idea Validation Deliverable: Vision and Scope document Business analysis kicks off the process, diving deep into business needs and requirements. This initial stage refines the project cost estimate and prevents budget overruns by providing a detailed breakdown of requirements, functionalities, and design elements. The key deliverable at this stage is a Vision and Scope document. It presents the overarching vision, purpose, and desired outcomes of the project, plus work that needs to be done. An experienced business analyst can evaluate the technical feasibility of a concept from their analysis. If there's uncertainty, they may initiate a Proof of Concept (PoC) to validate the idea, where the team develops a simplified version of crucial functionalities. This early detection of potential limitations by business analysis enables a more efficient and cost-effective software development process. Insights from business analysis and the PoC can prompt stakeholders to start with a Minimum Viable Product (MVP) development. Using a roadmap from the business analyst, the development team crafts an initial, feedback-eliciting version of the software. This MVP paves the way for future development and allows the business analyst to assess the idea's practicality and identify potential for a full-scale software development project. Business analysis during the discovery phase involves: Exploring the business context. Business analysts delve into the business background, outlining stakeholder profiles and identifying their key interests, values, expectations, and limitations. Evaluating business requirements. Business analysts conduct an in-depth examination of business opportunities, major strengths, and owner's challenges (using SWOT analysis). They also set business goals, establish success metrics, and identify potential risks. Defining the solution vision. This vision provides a contextual framework for decision-making throughout the product development lifecycle. It involves selecting system components, creating process diagrams, and wireframes, and outlining major features, dependencies, and both functional and non-functional requirements. Determining scope and constraints. This encompasses the scope for initial and subsequent releases, as well as the product backlog legend and roadmap. It also includes features not currently planned to include in the product (these may be under consideration but are not on the roadmap yet). Shaping Product Delivery Through a Detailed Development Roadmap Deliverable: the Software Requirements Specification The primary aim of business analysis during product delivery is to ensure precise software development. By creating comprehensive software requirement specifications, business analysts work to prevent misunderstandings when active software development begins. Key responsibilities of a business analyst during Product Delivery: Building the product backlog. By providing detailed functional specifications based on gathered requirements, a business analyst lays the groundwork for a comprehensive backlog and action plan. Establishing acceptance criteria and test plans. Defining acceptance criteria is as crucial as formulating user stories. These criteria outline the conditions to assess if a feature meets stakeholders' and end-users' expectations. Together with the product manager, a business analyst shapes these criteria and also develops test plan requirements for later software testing. Planning the subsequent releases. The business analyst determines the extent of business issues to address, modify, complete, or remove based on feedback, forming the backlog for a new development round. Developing user training materials. Once software programs or applications are developed, a business analyst creates user manuals or training materials. Which Tools Can Empower Your Business Analysis? Achieve your business goals without overstepping your budget and deadlines through key business analysis techniques and tools. Prototypes and Diagrams. It's important for business analysts to select the right prototyping tools tailored to the project's specific needs, stakeholder preferences, and the level of interactivity required for successful idea validation. Popular tools include Figma, Microsoft Visual Studio, Adobe XD, InVision, and others. Management and Communication. Strong management and communication skills enable a business analyst to navigate complex stakeholder relationships effectively, foster collaboration, and ensure project goals are understood and met. To maintain transparency and consistent communication, business analysts often use project management tools like Confluence, Jira, ClickUp, or any preferred tools of the client. Hire BA experts for financial software business analysis For nearly two decades, our team has been utilizing best practices in business analysis, helping clients navigate their business challenges and successfully deliver digital products. Each business analyst in our team brings essential competencies that consistently drive excellent results. Essential Skills for Your FinTech Business Analyst Communication Ability to listen actively and understand client requests and project ideas Effective written and verbal communication, primarily in English Strong consulting and interpersonal communication skills Facilitation of communication across departments Proficiency in translating technical information into language that business stakeholders can understand Analysis Strong analytical thinking and problem-solving abilities Accurate and detail-oriented reporting skills Competence in business analysis techniques and best practices Familiarity with business structure Understanding of the digital landscape of banking, investing, insurance, and risk management Proficiency in process modeling Skill in stakeholder analysis Technology Familiarity with SCRUM to streamline team-oriented projects Knowledge of databases, data gathering and storage processes Whether you're launching a new product or exploring ways to enhance or expand your existing system, reach out to our team. We offer: In-house business analysts, project managers, and Scrum masters with extensive hands-on experience in the financial domain Dedicated, cross-functional teams focused on end-to-end software product development Rapid team extension with senior tech specialists for flexibility and adaptability A strong business-oriented and proactive problem-solving approach Broad experience across multiple domains, including logistics, retail, agriculture, healthcare, education, energy, and publishing A proven track record of successfully delivering projects Let's Discuss Your Case Today! Frequently Asked Questions

Converting Business Problems into an Analytics Solution Organizations have goals like making more money, getting new customers, selling more, or cutting down on fraud. In a data analytics project, it's really important to first understand the problem the organization wants to solve. Then, figure out how a predictive analytics model, built using machine learning, can provide insights to help solve this problem. This step is all about creating the right analytics solution and is the key part of the Business Understanding phase in the project. Fraudulent Claim Prediction A predictive analytics model predicts the likelihood of fraud in insurance claims. It analyzes patterns in past insurance claims data, including both fraudulent and non-fraudulent claims, to identify indicators of fraud. To train the model, it would require a large dataset of insurance claims that have been classified as fraudulent or non-fraudulent.  The model would use the data to learn patterns and correlations that are often seen in fraudulent claims. For example, it might find that claims filed immediately after a policy change or claims for certain types of incidents are more likely to be fraudulent. Once the model is trained, it can be applied to new claims. Each claim would be given a score representing the likelihood of it being fraudulent. This is typically done on a scale, where a higher score indicates a higher likelihood of fraud. Claims that receive a high fraud likelihood score would be flagged by the system. This doesn't mean they are certainly fraudulent, but they have characteristics that warrant closer inspection. By using the model to prioritize which claims are investigated, the company can focus on the most suspicious cases. This targeted approach is more efficient than random checks or trying to investigate a large number of claims. This approach will increase the detection of fraudulent claims, thereby saving the company money and protecting resources. This could also deter fraud over time, as potential fraudsters realize that the chance of being caught is higher. The feasibility The key requirement for successfully implementing a claim prediction analytics solution in an insurance company is the business's capacity to provide database of historical claims marked as fraudulent and non-fraudulent, with the details of each claim, the related policy, and the related claimant. The prioritization mechanism should  identify and flag certain claims as high priority and operate within the existing timeframe for handling claims.  If the insurance company already has a claims investigation team, the feasibility study would assess how the team currently operates and how they would adapt to using a new system. High Risk Policyholders Prediction The primary goal is to predict the likelihood of a member (policyholder) committing fraud in the near future. This preemptive strategy aims to identify potential fraud before it occurs, rather than reacting to it after the fact. Running the model, for example, quarterly allows for regular updates on the risk profiles of members.  The model would likely use historical data, including past claims, behavioral patterns, policy changes, payment history, and other relevant data points. Advanced analytics and machine learning algorithms would analyze this data to identify patterns or behaviors that have historically been indicative of fraud. The model assigns a risk score to each member, indicating their propensity to commit fraud. Members with higher scores would be flagged as high risk. Based on this risk assessment, the company might contact the policyholder with a warning to with some kind of canceling their policies. By identifying and addressing potential fraud proactively, the insurance company could save significant amounts by preventing fraudulent claims. This approach could also deter potential fraudsters if they are aware of the company's proactive measures. The feasibility The feasibility of the proposed analytics solution for detecting potential fraud risks among members depends on several key conditions being met. Here are scenarios where the solution would be considered feasible. The organization has: the ability to link every claim and policy to a specific member and maintain historical records of policy changes. the operational capacity to conduct detailed analyses of customer behavior every quarter. a skilled team adept at maintaining positive customer relations, even when discussing sensitive issues like fraud. The organization should be well-versed in relevant legal and regulatory standards, such as privacy laws, and has mechanisms in place to ensure compliance. Fraudulent Intent of an Applicant Prediction This is a strategy aimed at identifying potential fraudulent activity at the earliest stage – when a policy application is submitted.  The primary goal of the model is to assess the likelihood of a new insurance application resulting in a fraudulent claim in the future. This preemptive measure is aimed at fraud prevention rather than detection after the fact. To make accurate predictions, the model would analyze a variety of data points. This could include information provided in the application, historical data of similar policies, patterns identified in past fraudulent claims, and possibly external data sources (like credit scores or public records). Each application would be screened by the model, assigning a risk score indicating the likelihood of a future fraudulent claim. Applications that score above a certain risk threshold could be flagged for further review or potentially rejected. The feasibility Here are scenarios where this solution would be considered feasible. The organization: has access to a collection of claims data, classified as either fraudulent or non-fraudulent, spanning many years, given the potential long interval between policy applications and claim submissions. have the capability to link each claim to the original application details. must have the capacity to integrate the automated application assessment process seamlessly with the existing application approval processes. Exaggerated Insurance Claim Prediction A common problem in insurance is claims where the requested payout is higher than what is justifiable. When an insurance company suspects a claim is over-exaggerated, they conduct an investigation. This process is resource-intensive and costly. The idea is to develop a machine learning model that predicts the likely payout amount based on historical data of similar claims and their outcomes. The model would use historical claim data, including the nature of the claim, the amount initially claimed, the results of any investigations, and the final settled amount. When a new claim is filed, this model can be run to estimate the likely legitimate payout amount.  Instead of going through the full investigation process, the insurer could offer the claimant the amount predicted by the model. This would be a faster, less costly process than a full investigation. The feasibility The solution will be feasible in scenarios where the following conditions are met. The organization: have access to information on the original amount specified in a claim and the final amount paid out.  needs the operational capacity to act on the insights provided by the model. This includes making offers to claimants, which assumes the existence of a customer contact center or a similar mechanism for direct communication with claimants. In this article, we are working under the assumption that following a review of its feasibility, the decision was made to move forward with the claim prediction solution. This involves developing a model capable of predicting the likelihood of fraud in insurance claims. Designing the Analytics Base Table The core of the model's design involves the creation of an Analytics Base Table. This table will compile historical claims data, focusing on specific features that are likely indicators of fraud (descriptive features) and the outcome of whether a claim was ultimately deemed fraudulent (target feature). The design of the Analytics Base Table is driven by the domain concepts. Domain concepts are the fundamental ideas or categories that are essential to understand a particular domain or industry.  Each domain concept translates into one or more features in the Analytics Base Table. For instance, the domain concept of "Policy Details" might be represented in the table through features like policy age, policy type, coverage amount, etc. The identification of relevant domain concepts is a collaborative effort involving analytics practitioners and domain experts within the business. The general domain concepts here are:  Policy Details. Information about the claimant’s policy, including the policy's age and type. Claim Details. Specifics of the claim, such as the incident type and the claimed amount.  Claimant History. Historical data on the claimant's previous claims, including the types and frequency of past claims. Claimant Links. Connections between the current claim and other claims, particularly focusing on repeated involvement of the same individuals in multiple claims, which can be a red flag for fraud. Claimant Demographics. Demographic information of the claimant, like age, gender, and occupation. Fraud Outcome. The target feature, which is derived from various raw data sources, indicating whether a claim was fraudulent.

What is Truewind?  Truewind is generative AI-based company based in San Francisco, raised a total funding of $17M over 2 rounds from 15 investors (Rho Capital, Thomson Reuters Ventures, Pathlight Ventures, Fin Capital, and Y Combinator). It has over 100 customers, including accounting firms like EisnerAmper and Frank Rimerman. How Truewind Uses AI for Accounting?  Transaction Classification Transaction classification (coding each transaction to the proper account, department, etc.) is a core bookkeeping step and a standard part of every accounting close process. All financial records depend on correctly classifying transactions in the general ledger. This includes identifying the payee, determining the account category (expense vs asset), and matching payments to invoices. Virtually 100% of accounting workflows involve transaction coding in some form. Every industry and company size (beyond perhaps the smallest cash-only operations) requires sorting of transactions into accounts. While the complexity may vary (a tech startup vs a manufacturing enterprise), the act of categorizing transactions (whether manually or via software rules) is universal in accounting. Bookkeepers and accounting staff are the primary people performing this workflow. Coding transactions is a repetitive, high-volume task that can consume a large portion of an accountant’s time. Accountants often spend hours identifying payees, choosing accounts, and adding tracking classes for thousands of transactions. Traditional accounting software offers limited automation, often requiring manual rule creation and constant oversight. This means the process is prone to errors (misclassified expenses) and drudgery, making it one of the more burdensome parts of bookkeeping. For transaction-heavy businesses, this pain is acute. In fact, automating even 90% of high-volume items (like credit card charges) saves substantial time and reduces errors, underscoring how much effort manual classification normally takes. Truewind AI classifies each transaction before it is synced (posted) into the client’s accounting system. It assigns the correct category (expense type or asset type), payee (vendor or merchant name), class (department, location, project), etc. Truewind also gives explanations and a confidence score, flags exceptions or low-confidence transactions for manual review, and allows bulk approval or review. The accountant opens their dashboard and finds that most transactions are classified and posted, exceptions are neatly flagged with explanations, and the ledger is nearly closed. All they need to do is review a small handful of items and approve. Transaction auto-classification using historical data is a classic LLM classification task. AI can achieve high accuracy (over 90%) in context-aware classification for this workflow. The manual coding step takes up to 60–70% of the total time spent on the transaction classification workflow. After automation, this drops to 5–10%, leaving only the review of flagged items. The AI classifies, flags exceptions, shows transactions in a structured format, and allows quick syncing to the ledger. The Truewind demo screenshot shows automated classification of bank and credit card transactions by category, payee, and class, with confidence indicators and review options before posting to the accounting system. Accrual Automation Accrual workpapers are a fundamental component of the month-end close in accrual accounting. They are used to record expenses and revenues in the correct period (prepaid expenses, accrued liabilities), ensuring compliance with GAAP/IFRS. Any company using accrual basis accounting (virtually all beyond the very small cash-basis businesses) must prepare such workpapers each period, especially subscription-based businesses requiring precise monthly accruals. Corporate accountants and controllers on accounting teams prepare and use accrual workpapers. This includes staff accountants compiling entries for accrued expenses or deferred revenue, and accounting managers or controllers reviewing them. Audit teams also examine these workpapers during financial audits, though the creation is done by the accounting department. Maintaining accrual schedules manually is time-consuming and error-prone. Accountants often juggle disjointed Excel spreadsheets for items like prepaid expenses and fixed asset depreciation. It’s considered a tedious workload that adds to close deadlines. Truewind automates accrual workpapers. AI prepares a clean, structured, audit-ready accrual package that’s 95% done, where the accountant only needs to double-check flagged items and click "approve". The system uses AI to read the transaction description and decide whether it's a prepaid expense that should be spread over future periods or a regular expense to book right away. This helps make accrual entries accurate and automatic. Preparation of accrual workpapers takes up to 50% of the entire accrual workflow, but after automation, this time is reduced by up to 10 times. At the same time, the entire accrual workflow becomes approximately 30–40% faster. Truewind AI automatically suggests accrual entries based on historical data and transaction patterns. The accountant simply reviews, selects, and confirms entries. AI Reconciliation Tool Reconciliation is a cornerstone of accounting and financial reporting. Financial records (like the general ledger) should match with external statements or subledgers. Common examples are bank reconciliations (matching the books to bank statements) and intercompany or subledger reconciliations. This process is fundamental for verifying that all transactions are recorded completely and accurately. In practice, every period-end close includes multiple reconciliations to catch discrepancies. Any organization that keeps books performs reconciliations regularly. Mid-size and large companies typically reconcile every bank account, credit card, and key balance sheet account each month. Even smaller companies perform at least bank and cash reconciliations. Accountants and controllers are responsible for reconciliations. Staff accountants or accounting analysts usually do the detailed matching. For example, ticking and tying transactions in bank recs or reconciling subledger reports (AR, AP, inventory) to the general ledger. Accounting managers or controllers then review and sign off on these reconciliations. External auditors also heavily scrutinize reconciliations during audits to verify that balances are supported. Reconciliation is a major bottleneck in the month-end close, often requiring hours or even days of manual work to match transactions line-by-line in Excel or other tools. High-volume accounts (such as cash or intercompany clearing accounts) make the process even more time-consuming, forcing teams to work late nights just to complete reconciliations. This error-prone, stressful “ticking and tying” process is a key reason why 82% of accountants view the close negatively, according to surveys. Truewind is currently working on AI-powered matching and anomaly detection, but it’s not in production yet. AI Contract Management Software Contract management is about aligning financial records (revenue recognition, lease liabilities, etc.) with contract terms (start/end dates, payment terms, performance obligations, etc.). For example, under revenue recognition standards (ASC 606 / IFRS 15), accountants must identify all key contract terms to allocate and time the revenue properly.  This workflow is essential for software, telecom, construction, leasing, and other industries. Many large companies have material contracts (SaaS subscriptions, multi-year sales agreements, vendor contracts, leases), so they require this process.  Often a technical accounting or revenue recognition team reviews customer contracts to determine how to record revenue or expenses. They coordinate with legal or sales operations to obtain contract details. Auditors also pay close attention to contract accounting (for example, checking that revenue is recognized according to the contract terms). Extracting key data from contracts is typically manual: accountants must read lengthy agreements and enter key details into spreadsheets or an ERP. AI streamlines contract management by employing optical character recognition (OCR) and natural language processing to automatically extract key terms from complex sales contracts. For example, the system pulls out payment schedules, renewal dates, deliverables, cancellation clauses, and other critical data directly from the contract documents. Rather than an accountant manually poring over each PDF, AI scans the text and populates those details into the accounting system or workpapers. Complex Contracts Complex contract management is an extension of contract management for businesses with highly customized or multi-element contracts. It covers scenarios like contracts with multiple performance obligations, custom pricing & billing terms, contract amendments, and revenue that must be recognized over time or under various conditions. Managing these manually is difficult, as revenue often needs to be broken into multiple components over time. For example, a SaaS company with custom contracts splits a contract into software license revenue, service revenue, and subscription revenue over different periods. Enterprise software companies, telecommunications, defense contractors, or any industry offering configurable bundles and multi-year deals have complex contracts.  Complex contracts are one of the most challenging areas of accounting. Many ERPs don’t fully support intricate arrangements out of the box, so companies resort to manual processes or expensive add-on systems. The workload involves reading through unique contract terms, manually configuring billing or revenue schedules, and constantly updating them for contract changes (amendments, renewals).  AI platform ingests a complex contract and then automatically generates the necessary sales orders or accounting entries directly in the ERP. For example, if a contract has multiple components (subscription fee, one-time setup fee, future rate increases, etc.), the system parses those details and creates accurate billing schedules. Truewind is currently working on AI-powered contract management, but it’s not in production yet. AI automates complex processes and improves decisions in finance and accounting industries. If you're looking to build an AI system, our guide on AI classification model costs breaks down key factors that influence development budgets and strategic AI investments. For a real-world example, see how BloombergGPT leverages AI for financial document classification, sentiment analysis, and more. Automation of Flux Analysis in Accounting Flux analysis (fluctuation analysis) is a common process during financial close and review. It compares account balances or financial line items between periods (month-over-month or year-over-year, or against budget) and analyses significant variances. In many organizations, flux analysis is part of internal controls or audit preparation, as management and auditors ask, “Why did this number change so much from last period?” Therefore, it’s a standard analytical step in the accounting/FP&A workflow for medium and large companies. Public companies, for example, must explain period-to-period changes in financial statements (Management Discussion & Analysis), so flux analysis is mandatory for them.   Flux analysis is used by those who need insight into financial changes: corporate accounting teams produce the analysis, and CFOs, controllers, audit committees, and auditors consume the results. Flux analysis can be quite tedious and is often cited as a pain point, especially because it’s traditionally done with a lot of manual work in spreadsheets. Accountants export trial balances to Excel, compute variances, and then manually write explanations or investigate transaction details for the differences. This process is time-consuming, and it becomes frustrating when late adjustments require re-doing the analysis.   Truewind is currently working on automating flux analysis by using advanced algorithms and AI, but it’s not in production yet. Pro-forma Forecasting Pro-forma and forecasting tools are used to create projected or “as if” financial statements (for example, forecasting the next quarter’s income statement, or combining entities for a what-if scenario). While not part of the ledger bookkeeping itself, they rely on accounting data and are often maintained by finance teams in conjunction with accounting. Many accounting departments (especially in firms that offer client advisory or in family office contexts) include forecasting as a service to provide insight to management.  Nearly all public companies and many mid-size to large firms use these tools for budgeting, cash flow forecasting, and strategic planning, such as annual budgets and fundraising. In accounting firms, offering projections and advisory is a value-add, especially for larger clients. So while not a required step for compliance, in practice, most companies perform this workflow to guide decision-making. In a corporate setting, the FP&A (Financial Planning & Analysis) department typically takes actual accounting data and projects it forward, and these are the primary users of forecasting software.  Financial forecasting and pro-forma budgeting are quite challenging. The pain comes from dealing with uncertain variables, large data sets, and the limitations of manual tools. Many teams still use spreadsheets for budgeting. According to a CFO study, 56% of finance leaders said the growing complexity of forecasting and budgeting is their greatest internal challenge. Common pain points include: consolidating data from multiple sources, updating forecasts for actual results, and running multiple scenarios without a good system. If done manually, producing a rolling forecast or pro-forma statements can consume a lot of time and still yield inaccurate results, making it a notable pain area in finance. Truewind is currently working on a cash flow projection feature that uses real-time data from the books to predict future cash positions. The system employs predictive analytics and machine learning to analyze historical cash patterns, upcoming payables/receivables, and other inputs, and then produces a forecast. However, it’s not in production yet. Intercompany Reconciliation Automation For companies with multiple legal entities, managing intercompany transactions is a critical part of the accounting and consolidation workflow. These financial dealings between subsidiaries or business units under the same parent, such as sales of goods from one subsidiary to another, or shared expenses must be recorded in each entity’s books and then eliminated or adjusted upon consolidation so that they don’t inflate the group’s overall financials. Intercompany accounting also involves transfer pricing adjustments, foreign exchange for cross-border intercompany, and reconciliation of intercompany balances. It’s considered one of the most complex routine tasks in finance because of the volume and need for precision. For any sizable or global company with subsidiaries, this process is absolutely required.  In fact, many large enterprises have dozens or hundreds of entities and thus heavy intercompany activity (e.g. cross-charges, intercompany sales, loans between entities). In those environments, intercompany accounting is unavoidable and happens every month. Nearly all multinational corporations must deal with it (often at a large scale, since intercompany entries are multiples of external transactions in volume).  Accounting teams at multinational or multi-entity companies, including consolidation accountants, corporate controllers, and treasury or tax specialists, use this workflow. The process starts with local entity accountants recording intercompany invoices or transfers, but the critical part is handled by corporate accounting during consolidation (to reconcile and eliminate those intercompany balances). Sometimes a company has a dedicated “Intercompany Accountant” or a team focused on intercompany reconciliations. The tax department also gets involved because transfer pricing (pricing of intercompany sales) has tax implications.   Intercompany accounting is widely recognized as a headache for finance teams. Surveys have found that 96% of finance stakeholders report challenges with intercompany processes, and an astounding 90% said their staff pull all-nighters at least once a year solely due to intercompany issues. The pain comes from difficulties in matching transactions between entities (discrepancies in timing or amounts), dealing with different currencies and exchange rates, and ensuring all parties record the transactions consistently. Overdue intercompany reconciliations can linger for years, causing uncertainty and even risk of audit issues. Nearly half of companies report that unresolved intercompany imbalances create business uncertainty and risk of compliance problems.   AI and intelligent rules automatically identify intercompany pairs. For example, if one entity records an intercompany sale, it finds the corresponding purchase in the other entity. During consolidation, AI automatically eliminates those intercompany entries so they won’t double count in the consolidated financials. This ensures compliance with accounting standards (GAAP/IFRS requires that intercompany revenue/expenses be removed) and saves a huge amount of manual work. The system also manages currency conversion and exchange rate adjustments in real-time, which is a big part of intercompany for global companies. Truewind is currently working on intercompany reconciliation automation with AI, but it’s not in production yet. How Belitsoft Can Help Belitsoft offers standout software engineers to build AI and machine learning applications, as well as data-intensive distributed systems for finance and accounting. We also provide exceptional full-stack engineering services to architect and develop software products that use AI technology to reimagine traditional bookkeeping and finance processes. Our developers create web applications and LLM applications using both custom LLMs and the OpenAI API integrated with vector databases. They also build and manage the technical infrastructure supporting databases, servers, and APIs. Partner with Belitsoft and automate your accounting workflows with Generative AI. By outsourcing our software developers, you eliminate manual entry, receive audit-ready outputs and generate accrual workpapers, financial statements, and variance reports in minutes ready for an accountant’s review to flag items, not rebuild them. Contact us to discuss your project.

What Is a “Financial LLM”? A financial LLM is a large language model, trained or fine-tuned on financial data, to be tailored for the finance domain, able to answer questions or generate content with an understanding of financial context, instruments, and regulations. Such models grasp industry jargon (tickers, regulations, accounting terms), handle numeric and tabular context, and comply with financial regulations in their outputs.  Organizations seek to apply the power of GPT-style models to banking, markets, insurance, and financial analytics while incorporating domain expertise and control. General-purpose LLMs (like GPT-4) lack certain finance-specific knowledge or precision, and companies have begun developing specialized “FinLLMs”.  BloombergGPT was one of the first large models trained specifically on a wide range of financial data (in addition to general text). Core Features and Capabilities of Financial LLMs Financial LLMs can  answer questions, analyze and  summarize  text, classify sentiment or intent, check compliance, and produce financial writing.   Financial LLMs are used to generate content tailored to finance needs: draft research reports, write personalized portfolio explanations for clients, compose client emails, or generate financial news articles. Such a model writes in a style and context that financial professionals and customers expect. Question Answering on Financial Knowledge It's a chat assistant that understands your world and speaks your financial language, backed by actual data. Financial LLMs help answer questions like “What happened in Company X’s Q3 results?” or “What does Basel III actually require?” not by guessing, but by pulling answers from internal docs, or research.  They’re built to understand finance, and talk like a human, whether you’re a banker checking policy, or an investor tracking the market.  Most financial LLMs now prioritize auditability, because in this space you need to show where the answer came from. No black box. Just traceable output linked to source. Document Summarization and Report Generation It summarizes lengthy financial documents (research reports, earnings call transcripts, 10-K filings, insurance policies) into concise, clear narratives.  A financial LLM produces an executive summary of a 100-page annual report or distils key points from an earnings call in a few sentences. This is a highly valued feature given the volume of texts.  JPMorgan’s internally-developed DocLLM is designed to process visually complex documents and extract key information, providing summaries and answering questions about the content. Automating report generation (writing first drafts of market commentary or credit memos) is another capability of LLM. Sentiment Analysis and Market Insights LLMs are getting better at pulling signals from fast sources like news, Twitter, and analyst notes. They can tag headlines or posts as positive, negative, or neutral for a stock. That's basic for a fintech LLMs.  Regulatory Compliance and Risk Assessment Finance is heavily regulated. LLMs in this space need to support compliance and risk, not just generate text. Most real deployments use retrieval augmentation or guardrails to keep answers accurate and policy-aligned. FinLLMs are used to cross-check text against rules - for example, scan loan docs for compliance issues, flag SEC or FINRA violations, and pull policy red flags from internal communications. Financial LLMs are also used for risk checks. They parse financial statements, credit history, and reports to surface red flags or consolidate exposure data. Domain-tuned models are safer, because they stay within boundaries: no leaks, no speculation, no policy violations. Financial Data Extraction and Synthesis Extracting structured data from unstructured financial text is another core capability.  An LLM ingests a pile of earnings reports or claim forms and pulls out key fields (revenues, dates, loss amounts, etc.), performing data entry and aggregation. These models can then synthesize data across sources by aggregating and comparing data from multiple quarterly reports to answer “How did revenue grow quarter-over-quarter?”.  They can fill out templates or spreadsheets with information gathered from documents. This capability supports use cases like automating due diligence (consolidating data on a company from various filings) and feeding downstream analytics or models. FinBERT (financial sentiment analysis) FinBERT is a specialized open-source BERT-based model trained on financial text (news, filings, social media) for sentiment analysis. FinBERT was released years ago and hasn’t been actively updated, but 2024 year’s  paper shows that it’s still useful, especially when fine-tuned and combined with a time-series model like LSTM. FinBERT hasn’t been updated in years, however this 2024 paper shows it’s still usable when fine-tuned and combined with other models like LSTM. FinBERT is based on BERT, trained on financial text to work as a sentiment classifier, not a full LLM by current standards. The study shows it still holds as a reliable component inside a larger pipeline. If you work with financial news and need sentiment signals, you can fine-tune it on your own data and feed the output into whatever model you already use (forecasting, scoring, classification). Load the model, run inference on news or filings, and map the output to positive, neutral, or negative. Output can be used as a feature in trading logic: entry/exit signals, risk filters, portfolio weighting. Use cases: news sentiment on equities, regulatory sentiment for risk exposure, general signal extraction from contracts or disclosures. For example, FinBERT can be used with QuantConnect, a cloud platform for developing, testing, and deploying algorithmic trading strategies across equities, FX, futures, options, derivatives, and crypto. FinGPT (financial sentiment analysis) FinGPT is an open-source financial large language model (LLM) developed by the SecureFinAI Lab at Columbia University for sentiment analysis, market trend prediction, and financial report summarization. FinGPT is a model built using transformer architecture. The model itself hasn't been updated since 2023 due to lack of funding, but it's still being actively used. For example, in 2025, there was news about fine-tuning this model to do extra tasks like financial risk prediction via audio analysis or end-to-end trading.  FinGPT v3.3 shows that a fine-tuned open-source model can outperform GPT-4 and earlier domain-specific models like FinBERT on narrow financial tasks without needing GPT-4 scale or cost.

Fintech Segments That Hire Business Intelligence Consultants Neobanks (Digital-Only Banks) Digital banks don’t have branches or tellers. Their only real-world footprint is the data trail their users leave behind: login patterns, spend habits, churn events, drop-off flows.  BI in a neobank environment answers questions the CEO is already asking: Where are we “bleeding” in the funnel? What’s our fraud exposure today, not last quarter? Which users should we invest in retaining and which should we quietly let churn? Which features are sticky? And BI doesn’t just report; it drives actions. A consultant might flag a spike in failed logins in a specific zip code, triggering fraud mitigation protocols. Or identify a high-performing onboarding path that can be replicated in a new feature. These are the kinds of insights that move CAC, LTV, and NPS: the CEO-level numbers that ultimately determine valuation. Growth, Retention, and Spend Efficiency  Neobanks compete on slim margins. Every ad dollar has to work. That’s why BI consultants are often embedded with growth teams, analyzing: Which acquisition channels yield high-LTV customers? What’s our CAC by segment or cohort? Which incentives convert one-time users into daily ones? This isn’t just about visualizing the funnel but optimizing it. BI experts connect the dots between marketing analytics, in-app behavior, and user segmentation: you’re not just acquiring users but the right ones. In markets with high churn and expensive acquisition, that’s the difference between Series D and getting delisted. BI Drives Product  Product intuition in a digital bank is incomplete without analytics. BI consultants feed product managers the behavioral fuel they need to prioritize: Which parts of the registration flow lose the most users? Are users really using that new savings feature, or just clicking in and bouncing? Does adding another KYC step kill conversion or reduce fraud? Consultants surface these patterns early - often before they show up in revenue or support tickets. That’s what makes them so powerful. They shift teams from reacting to preempting. Chime is the example here. Their BI and analytics team sits with product and marketing: building the metric frameworks that define success, guiding feature rollout, and shaping long-term roadmap decisions. BI is part of their DNA, not an afterthought. In 2025, the firms that lead in fintech are insight machines. And the companies who know how to operationalize BI, not just to monitor, but to inform and optimize - are the ones building market advantages. When your fintech team needs more than just reports but real-time analytics, our experienced BI consultants and development team turn your data into action. We build custom dashboards, predictive models, and decision-ready analytics tools tailored to your product and users. Payment Platforms (Payments and Digital Wallets) The payment stack looks deceptively clean to the customer: swipe, tap, done. But under the hood? It’s a spiderweb: acquirers, processors, fraud engines, banks, FX services, regional gateways, and APIs that all have to handshake in milliseconds. Every one of those layers produces data. And unless you have the BI expertise to aggregate, reconcile, and interpret it - you’re blind. BI consultants in this space are solving hard problems: Why did a merchant’s authorization rate dip 3% last Thursday? Why is a specific gateway showing latency spikes during peak hours? Which payment methods are growing fastest by geography and margin? Without that clarity, you’re firefighting. Operational Intelligence Payment firms build dashboards tracking: Success vs. failure rates, by method and region Average transaction value and volume Latency by gateway, issuer, or network Error codes tied to specific banks or devices This data isn’t just for engineers - it’s for executives. A spike in transaction failures in Latin America? BI surfaces it first. A partner gateway degrading slowly across a week? The BI team shows the trend before support tickets pile up. This visibility directly protects revenue - by detecting performance issues before they become churn events. Stripe, for example, built live dashboards that track the global health of its payments infrastructure, not just for technical health, but business impact. That’s the difference between passive monitoring and business-aware analytics. Revenue, Risk, and Optimization BI stitches together what most organizations still treat as separate: Revenue insights: Who’s transacting the most? Which cohorts drive margin? User behavior: Which payment methods convert best by segment or region? Fraud detection: What anomalies are just edge cases - which are early signals? The best BI consultants bridge these questions in the same dashboard. They help risk teams build fraud scoring models without killing user experience via false declines. They help product teams understand which payment options are underperforming and why. They help revenue teams isolate profitable merchant tiers and optimize pricing. For companies like PayPal, these BI-driven insights directly inform which partnerships to prioritize, which UX flows to AB test, and which countries to double down on for growth.  BI Is the Compass Whether it’s Stripe expanding into new markets, or Square introducing BNPL features, those moves are backed by BI: What’s our volume by vertical in this region? What’s the fraud profile for the top 5 banks in the market? Can our infrastructure sustain another 100k users per day at current latency? BI teams provide the answers: in dashboards, in forecasts, in decision memos. They don’t just answer what’s happening. They model what’s next. Stripe’s internal BI team builds the metrics infrastructure that leadership runs the business on. They’re involved in product planning, operational readiness, and even feature deprecation, because everything touches the data. Lending Platforms (Digital Lending and BNPL) Credit Risk Isn’t Static From education history to bank cashflows, mobile phone usage to payroll APIs: the underwriting model is only as smart as the data behind it, and that’s where BI consultants come in. They surface correlations, test segment performance. They determine whether a borrower who scores 660 but has a recent college degree and three months of perfect neobank activity is a risk - an opportunity. Affirm’s entire underwriting model lives and dies by one question: what default rate are we accepting at this level of loan approval? BI teams track that in real time. The model may approve 70% of users, but if loss rates creep from 2.1% to 2.8%, someone has to catch it - and fast. That’s the job of BI.  Portfolios Need Radar Once loans are disbursed, it’s not just about waiting for repayment. It’s about active portfolio surveillance. Which cohorts are going sideways? Which geographies are softening? Is BNPL delinquency rising among Gen Z shoppers in fashion retail but not in travel? BI consultants power dashboards that answer these questions daily - by segmenting portfolio data across behavior, demographics, and payment patterns. Collections teams don’t blast everyone anymore. They target likely-to-cure segments first, based on repayment history and contact method effectiveness. That’s BI applied directly to recovery - turning analytics into dollars reclaimed. In many platforms, a 1–2% lift in collection rates across at-risk segments can unlock millions in preserved revenue. BI is how that happens. Growth That Pays for Itself Customer acquisition isn’t just a marketing function anymore, but it’s an analytical battlefield. CAC, drop-off rates, cost-per-funded-loan, funnel velocity - BI consultants run these models. And they’re not just measuring. They’re shaping targeting strategies. BI tells SoFi which ones bring good borrowers: high FICO, low churn, high cross-sell uptake. BI tells Upstart if a cleaner UX after A/B test of web pages increased completion from qualified users, not just more volume. Even pricing is analytics-driven. Want to bump conversion? Offer 1% lower APR, but only for segments with high predicted repayment likelihood. BI makes it possible to do that surgically, not by blunt discounting. This is where growth and risk get braided together, and BI is the unifier. Strategic BI Every lending decision has a downstream effect: risk, revenue, capital burn, regulatory exposure. And as markets fluctuate, capital costs shift, and borrower behaviors evolve: BI gives leadership the real-time radar to steer. You can’t afford to review performance quarterly. It has to be continuous recalibration. Upstart and Affirm are models of this in action. Their BI teams sit in daily standups with product, growth, and credit policy, pushing insights upstream into decision-making. When loss rates nudge, when default curves change, when new user behavior signals emerge, BI flags it before it shows up in charge-off reports. Insurtech (Insurance Technology Firms) CEOs leading insurtech ventures know that your value proposition is only as strong as your visibility into risk, claims, and customer behavior.  Pricing Risk is About Pattern Recognition Underwriting is the heart of the business. BI consultants here don’t just build dashboards. A BI-driven insurtech can analyze telematics, IoT feeds, weather models, historical claims, and demographic data, and then push it all into pricing models that can segment customers with precision. A user drives aggressively but only during the day? Adjust pricing accordingly. Claims spike in flood zones following two weeks of rainfall? Adjust exposure models in real time. A new cohort of Gen Z pet owners? Predict claims patterns before the actuaries catch up. This is where BI merges with predictive analytics.  Claims Are Where You Make (or Lose) Trust and Margin Claims management is where most insurers lose customer loyalty and money. It’s also where BI makes the biggest operational impact. BI dashboards monitor: Claim volume by region or cause Time to first contact, time to payout Approval vs. denial rates Anomalous behaviors or patterns that suggest fraud The key advantage? Proactive visibility. When a claims region is lagging, BI shows it. When a claim looks suspicious, BI flags it, not after the payout, but as it’s being processed. Lemonade has used this kind of data to deliver on its instant-payout promise, even as it scales.  From Mass Coverage to Micro-Personalization BI is also the engine behind product innovation. What riders are being added most? Which customer profiles are buying bundled coverage? Who’s likely to churn next quarter? This isn’t just CRM territory. It’s profitability intelligence: Which products deliver healthy loss ratios? Which customer segments drive margin vs. loss? Where can you push growth without spiking risk? Personalization in insurtech isn’t just a better quote flow. It’s using BI to match risk appetite with customer demand at scale. And BI doesn’t stop at customer insights. It drives capital allocation and regulatory posture. Whether it’s surfacing trends for board-level strategy or calculating reserve requirements for auditors - BI keeps the business compliant, informed, and agile. Lemonade: A Case Study in BI-Led Growth Lemonade didn’t just build an app. It built a BI platform that feeds product, pricing, marketing, and ops from a single source of truth. Their Looker-based system allows cross-functional teams to pull consistent KPIs, explore product performance, and spot new opportunities before competitors react. They didn’t guess at pet insurance or car insurance - they launched them based on customer data and BI-led opportunity mapping. That’s BI as product strategy - not back-office analytics. Wealthtech (Investment and Wealth Management Fintechs) AUM Is Not Just a Metric Your total assets under management (AUM) are the single biggest indicator of scale and trust. But AUM on its own is static. BI gives it motion: Where is AUM growing or shrinking: by cohort, by feature, by time of day? What’s the breakdown of recurring contributions vs. one-time deposits? How do performance returns compare against benchmarks and are users actually beating inflation? A strong BI layer doesn’t just report AUM. It explains it.  Betterment and Wealthfront are classic examples: they don’t just track daily balances. They correlate changes with user actions, product launches, or marketing campaigns. They know what’s driving growth, not just that it’s happening. Even trading spread revenue or advisory fees become BI artifacts. How much are you earning per user segment? Which services are most profitable per dollar of dev effort? Where is the cost-to-serve highest? In a market that’s increasingly margin-compressed, BI is your profitability microscope. Engagement Isn’t Just Retention Wealthtech lives and dies by active use. Inactive users don’t deposit. They don’t upgrade. They churn silently. BI helps you surface: Login and session patterns Feature interaction funnels Abandonment triggers (drop-off in funding flows or rebalancing features, etc.) You’re not just asking “how many users logged in today?” You’re asking: “which behaviors correlate with retention?” “Which feature launches actually move engagement?” “Where are people stalling in their first 30 days?” Wealthfront tracked how often users engaged with the app and used color-coded thresholds: green for healthy activity, yellow for drop-off, red for risk. Then they built features specifically aimed at improving those numbers. If your product roadmap isn’t shaped by this kind of BI telemetry, you’re iterating blind. You may be wasting dev cycles on features that look cool but don’t drive deposits or loyalty. Personalization Is the Monetization Engine All wealthtechs talk about personalized finance. Few deliver on it.  With the right BI systems, you can: Segment users by behavior, demographics, risk tolerance, financial goals Trigger personalized messaging, offers, or dashboard layouts Recommend the next best action: contribute, rebalance, upgrade A BI consultant might build a model that predicts which users are at risk of cashing out and trigger educational content or support follow-up before they go dark. Or you might run a segmentation analysis and discover that high-LTV users engage more with tax-loss harvesting tools, so you elevate that feature in the dashboard for similar users. Robinhood didn’t add crypto trading because someone had a hunch. They saw where user interest was spiking. BI flagged the signal, and the product followed. BI: From Compliance to Strategy in Real Time And then there’s the backend value: compliance. Regulatory reporting, audit trails, capital exposure - it all flows through the BI layer. The real upside is how BI aligns the whole business:   Product: “Which features actually move AUM?” Growth: “Which channels bring in the most profitable users?” Support: “Where are users stuck, and what’s causing ticket spikes?” Leadership: “Where should we invest headcount and capital next quarter?”   Betterment’s use of Looker dashboards to democratize visibility means every employee has access to real-time data. When everyone can see the score, everyone plays the game better. Blockchain/Crypto Firms BI as the Trading Floor Control Panel Crypto exchanges like Coinbase and Kraken operate more like infrastructure providers than traditional brokerages. Every second, they’re processing thousands of trades across dozens (or hundreds) of assets. BI consultants are the ones turning that firehose into intelligence. Key metrics tracked in real time: Volume by asset, trading pair, and region Liquidity and bid-ask spreads Order book depth and volatility Exchange fee revenue by customer segment Custodial asset value on-platform If trading volume on a specific token spikes, your infrastructure needs to scale. If liquidity dries up on a new pair, BI surfaces it before users feel it. If fees drop below profitability thresholds, BI raises the flag. And with on-chain activity now part of the data stack, BI teams even monitor blockchain inflows/outflows - spotting demand signals before they hit the platform. Your next most profitable trading pair? BI already saw it coming. Know Your Users - Or You’re Building for Ghosts Crypto platforms serve wildly different personas. The same interface may host: Passive holders checking price once a week High-frequency traders with custom APIs Users bridging tokens from L2s to mainnets NFT collectors Stakers and DeFi liquidity providers You can’t build one product for all of them. BI tells you who’s who - and what they want. At Coinbase, data analysts routinely cluster users by behavior — frequency, volume, asset mix, wallet age — and use those clusters to define roadmap priorities. New mobile features? Tailored for casual users. Advanced order types? Built for the top 5% of trading volume. This segmentation powers precision product strategy. Without it, you’re flying blind, building what you think users want — not what the data proves they’ll use. BI Is the First Line of Defense In crypto, the speed of fraud is fast. You don’t get weeks to detect patterns. You get minutes - if you’re lucky. BI teams in crypto companies are wired into: Anomaly detection ( sudden spike in withdrawals or trading from flagged IPs, etc.) Real-time exposure to volatile assets AML monitoring and suspicious activity pattern recognition KYC funnel conversion and identity risk scoring BI supports reporting, too — surfacing metrics for regulators, partners, and internal risk committees. Coinbase’s internal risk scores, lifetime value models, and fraud prediction systems are built off BI-led integrations between blockchain data, transaction logs, and user accounts. BI Isn’t just a Function  When Chainalysis decides which chains to support next, it’s not guessing. It’s analyzing: Market data demand User behavior across clients On-chain activity trends That’s BI, not product management alone. When Coinbase runs promotions or referral programs, they’re targeting users with modeled lifetime value curves — shaped by BI. In crypto, the feedback loop is faster, the cost of delay is higher, and the opportunity window is shorter. BI enables your team to react in time — or, more often, to act before the market moves. How Belitsoft Can Help Belitsoft is the technical partner fintechs call when they need BI that does more than visualize - BI that operates in real time, predicts what’s next, and supports business-critical decisions across risk, growth, and product. BI Consulting and Strategy Design Help fintech firms define KPI frameworks tailored to each segment: onboarding funnels for neobanks, fraud triggers for payment platforms, claim efficiency for insurtechs. Build BI roadmaps to connect siloed departments (product, risk, ops, marketing) through shared, actionable data. Custom BI Infrastructure Development Build data pipelines, ETL processes, and dashboards from scratch - using Looker, Power BI, Tableau, or open-source stacks. Integrate data from multiple sources (CRM, mobile apps, APIs, transaction logs, KYC systems) into a unified reporting platform. Behavioral Analytics & Predictive Modeling Implement machine learning models to predict churn, fraud, repayment likelihood, or upsell potential. Analyze user actions (logins, clicks, conversions, claims filed) to segment customers and drive retention or LTV. Embedded Analytics in Custom Fintech Platforms Build platforms with BI built-in — not just for internal reporting but to give users real-time views of their own data (AUM growth, spend insights, creditworthiness, etc.). Design admin dashboards for compliance, audit, or operational oversight. Risk, Compliance, and Regulatory Reporting Tools Automate report generation for audits, board meetings, and regulators. Ensure secure handling of sensitive financial/insurance data — complying with GDPR, HIPAA, PCI DSS, or other frameworks. Ongoing BI Operations and Support Offer BI-as-a-service: ongoing support for dashboard updates, data quality management, or metric tuning. Help internal teams become self-sufficient with data through training or embedded analysts. Partner with dedicated Power BI developers and BI consultants for fintech from Belitsoft who collaborate directly with your team. Our experts design secure, tailored analytics solutions, from Power BI dashboards to full-scale data systems, backed by deep technical and industry know-how. Contact for a consultation.

Why Financial Institutions Migrate Data Legacy systems are dragging them down Most migrations start because something old is now a blocker. Aging infrastructure no one wants to maintain, systems only one person understands (who just resigned), workarounds piled on top of workarounds. Eventually, the cost of not migrating becomes high. Compliance doesn’t wait New regulations show up, and old systems cannot cope. GDPR, SOX, PCI, local data residency rules. New audit requirements needing better lineage, access logs, encryption. If your platform cannot prove control, migration becomes the only way to stay in business. M&A forces the issue When banks merge or acquire, they inherit conflicting data structures, duplicate records, fragmented customer views. The only path forward is consolidation. You cannot serve a unified business on mismatched backends. Customer expectations got ahead of tech Customers want mobile-first services, real-time transactions and personalized insights. Legacy systems can’t provide that. They weren’t designed to talk to mobile apps, stream real-time data, or support ML-powered anything.  Analytics and AI hit a wall You can’t do real analytics if your data is trapped in ten different systems, full of gaps and duplicates, updated nightly via broken ETL jobs. Modern data platforms solve this. Migrations aim to centralize, clean, and connect data. Cost pressure from the board Everyone says "cloud saves money." That’s only half true. If you’re running old on-premises systems with physical data centers, licenses, no elasticity or automation …then yes, the CFO sees migration as a way to cut spending. However, smart teams don’t migrate for savings alone. They migrate to stop paying for dysfunction. Business wants agility. IT can’t deliver When the business says "launch a new product next quarter," and IT says "that will take 8 months because of system X," migration becomes a strategy conversation. Cloud-native platforms, modern APIs, and scalable infrastructure are enablers. But you can’t bolt them onto a fossil. Core system upgrades that can’t wait anymore This is the "we’ve waited long enough" scenario. A core banking system that can’t scale. A data warehouse from 2007. A finance platform with no support. It’s not a transformation project. It’s triage. You migrate because staying put means stagnation, or worse, failure, during a critical event. We combine automated tools and manual checks to find hidden risks early before they become problems through a discovery process, whether you’re consolidating systems or moving to the cloud. Database Migration Strategy Start by figuring out what you really have Inventory is what prevents a disaster later. Every system, every scheduled job, every API hook: it all needs to be accounted for. Yes, tools like Alation, Collibra, and Apache Atlas can speed it up, but they only show what is visible. The real blockers are always the things nobody flagged: Excel files with live connections, undocumented views, or internal tools with hard-coded credentials. Discovery is slow, but skipping it just means fixing production issues after cutover. Clean the data before you move it Bad data will survive the migration if you let it. Deduplication, classification, and data profiling must be done before the first trial run. Use whatever makes sense: Data Ladder, Spirion, Varonis. The tooling is not the hard part. The problem is always legacy data that does not fit the new model. Data that was fine when written is now inconsistent, partial, or unstructured. You cannot automate around that. You clean it, or you carry it forward. Make a real call on the strategy - not just the label Do not pick a migration method because a vendor recommends it. Big Bang works, but only if rollback is clean and the system is small enough that a short outage is acceptable. It fails hard if surprises show up mid-cutover. Phased is safer in complex environments where dependencies are well-mapped and rollout can be controlled. It adds overhead, but gives room to validate after each stage. Parallel (or pilot) makes sense when confidence is low and validation is a high-priority. You run both systems in sync and check results before switching over. It is resource-heavy, you are doubling effort temporarily, but it removes guesswork. Hybrid is a middle ground. Not always a cop-out, it can be deliberate, like migrating reference data first, then transactions. But it requires real planning, not just optimism. Incremental (trickle) migration is useful when zero downtime is required. You move data continuously in small pieces, with live sync. This works, but adds complexity around consistency, cutover logic, and dual writes. It only makes sense if the timeline is long. Strategy should reflect risk, not ambition. Moving a data warehouse is not the same as migrating a trading system. Choose based on what happens when something fails. Pilot migrations only matter if they are uncomfortable Run a subset through the full stack. Use masked data if needed, but match production volume. Break the process early. Most failures do not come from the bulk load. They come from data mismatches, dropped fields, schema conflicts, or edge cases the dev team did not flag. Pilot migrations are there to surface those, not to "prove readiness." The runbook is a plan, not a document If people are confused during execution, the runbook fails. It should say who does what, when, and what happens if it fails. All experts emphasize execution structure: defined rollback triggers, reconciliation scripts, hour-by-hour steps with timing buffers, a plan B that someone has actually tested. Do not rely on project managers to fill in gaps mid-flight. That is how migrations end up in the postmortem deck. Validation is part of the job, not the cleanup If you are validating data after the system goes live, you are already late. The validation logic must be scripted, repeatable, and integrated, not just "spot checked" by QA. This includes row counts, hashing, field-by-field matching, downstream application testing, and business-side confirmation that outputs are still trusted. Regression testing is the only way to tell if you broke something. Tools are fine, but they are not a strategy Yes, use DMS, Azure Data Factory, Informatica, Google DMS, SchemaSpy, etc. Just do not mistake that for planning. All of these tools fail quietly when misconfigured. They help only if the underlying migration plan is already clear, especially around transformation rules, sequence logic, and rollback strategy. The more you automate, the more you need to trust that your input logic is correct. Keep security and governance running in parallel Security is not post-migration cleanup. It is active throughout. Access must be scoped to migration-only roles PII must be masked in all non-prod runs Logging must be persistent and immutable Compliance checkpoints must be scheduled, not reactive Data lineage must be maintained, especially during partial cutovers This is not a regulatory overhead. These controls prevent downstream chaos when audit, finance, or support teams find data inconsistencies. Post-cutover is when you find what you missed No matter how well you planned, something will break under load: indexes will need tuning, latency will spike, some data will have landed wrong, even with validation in place, reconciliations will fail in edge cases and users will see mismatches between systems. You need active monitoring and fast intervention windows. That includes support coverage, open escalation channels, and pre-approved rollback windows for post-live fixes. Compliance, Risk, and Security During Migration Data migrations in finance are high-risk by default. Regulations do not pause during system changes. If a dataset is mishandled, access is left open, records go missing, the legal and financial exposure is immediate. Morgan Stanley was fined after failing to wipe disks post-migration. TSB’s failed core migration led to outages, regulatory fines, and a permanent hit to customer trust. Security and compliance are not post-migration concerns. They must be integrated from the first planning session. Regulatory pressure is increasing The EU’s DORA regulation, SEC cyber disclosure rules, and ongoing updates to GDPR, SOX, and PCI DSS raise the bar for how data is secured and governed.  Financial institutions are expected to show not just intent, but proof: encryption in transit and at rest, access logs, audit trails, and evidence that sensitive data was never exposed, even in testing. Tools like Data Ladder, Spirion, and Varonis track PII, verify addresses, and ensure that only necessary data is moved. Dynamic masking is expected when production data is copied into lower environments. Logging must be immutable. Governance must be embedded. Strategy choice directly affects your exposure The reason phased, parallel, or incremental migrations are used in finance has nothing to do with personal preference - it is about control. These strategies buy you space to validate, recover, and prove compliance while the system is still under supervision. Parallel systems let you check both outputs in real time. You see immediately if transactional records or balances do not match, and you have time to fix it before going live. Incremental migrations, with near-real-time sync, give you the option to monitor how well data moves, how consistently it lands, and how safely it can be cut over - without needing full downtime or heavy rollback. The point is not convenience. It is audit coverage. It is SLA protection. It is a legal defense. How you migrate determines how exposed you are to regulators, to customers, and to your own legal team when something goes wrong, and the logs get pulled. Security applies before, during, and after the move Data is not less sensitive just because it is moving. Testing environments are not immune to audit. Encryption is not optional - and access controls do not get a break. This means: Everything in transit is encrypted (TLS minimum) Storage must use strong encryption (AES-256 or equivalent) Access must be restricted by role, time-limited, logged, and reviewed Temporary credentials are created for migration phases only Any non-production environment gets masked data, not copies Belitsoft builds these controls into the migration path from the beginning - not as hardening after the fact. Access is scoped. Data is verified. Transfers are validated using hashes. There is no blind copy-and-paste between systems. Every step is logged and reversible. The principle is simple: do not treat migration data any differently than production data. It will not matter to regulators that it was "temporary" if it was also exposed. Rely on Belitsoft’s database migration engineers and data governance specialists to embed security, compliance, and auditability into every phase of your migration. We ensure your data remains protected, your operations stay uninterrupted, and your migration meets the highest regulatory standards. Reconciliation is the compliance checkpoint Regulators do not care that the migration was technically successful. They care whether the balances match, the records are complete, and nothing was lost or altered without explanation. Multiple sources emphasize the importance of field-level reconciliation, automated validation scripts, and audit-ready reports. During a multi-billion-record migration, your system should generate hundreds of real-time reconciliation reports. The mismatch rate should be in the double digits, not thousands, to prove that validation is baked into the process. Downtime and fallback are also compliance concerns Compliance includes operational continuity. If the system goes down during migration, customer access, trading, or payment flows can be interrupted. That triggers not just customer complaints, but SLA penalties, reputational risk, and regulator involvement. Several strategies are used to mitigate this: Maintaining parallel systems as fallback Scheduling cutovers during off-hours with tested recovery plans Keeping old systems in read-only mode post-cutover Practicing rollback in staging Governance must be present, not implied Regulators expect to see governance in action, not in policy, but in tooling and workflow: Data lineage tracking Governance workflows for approvals and overrides Real-time alerting for access anomalies Escalation paths for risk events Governance is not a separate track, it is built into the migration execution. Data migration teams do this as standard. Internal teams must match that discipline if they want to avoid regulatory scrutiny. No margin for "close enough" In financial migrations, there is no tolerance for partial compliance. You either maintained data integrity, access control, and legal retention, or you failed. Many case studies highlight the same elements: Drill for failure before go-live Reconcile at every step, not just at the end Encrypt everything, including backups and intermediate outputs Mask what you copy Log everything, then check the logs Anything less than that leaves a gap that regulators, or customers, will eventually notice. Database Migration Tools There is no single toolset for financial data migration. The stack shifts based on the systems involved, the state of the data, and how well the organization understands its own environment. Everyone wants a "platform" - what you get is a mix of open-source utilities, cloud-native services, vendor add-ons, and custom scripts taped together by the people who have to make it work. Discovery starts with catalogs Cataloging platforms like Alation, Collibra, and Apache Atlas help at the front. They give you visibility into data lineage, orphaned flows, and systems nobody thought were still running. But they’re only as good as what is registered. In every real migration, someone finds an undocumented Excel macro feeding critical reports. The tools help, but discovery still requires manual effort, especially when legacy platforms are undocumented. API surfaces get mapped separately. Teams usually rely on Postman or internal tools to enumerate endpoints, check integrations, and verify that contract mismatches won’t blow up downstream. If APIs are involved in the migration path, especially during partial cutovers or phased releases, this mapping happens early and gets reviewed constantly. Cleansing and preparation are where tools start to diverge You do not run a full migration without profiling. Tools like Data Ladder, Spirion, and Varonis get used to identify PII, address inconsistencies, run deduplication, and flag records that need review. These aren’t perfect: large datasets often require custom scripts or sampling to avoid performance issues. But the tooling gives structure to the cleansing phase, especially in regulated environments. If address verification or compliance flags are required, vendors like Data Ladder plug in early, especially in client record migrations where retention rules, formatting, or legal territories come into play. Most of the transformation logic ends up in NiFi, scripts, or something internal For format conversion and flow orchestration, Apache NiFi shows up often. It is used to move data across formats, route loads, and transform intermediate values. It is flexible enough to support hybrid environments, and visible enough to track where jobs break. SchemaSpy is commonly used during analysis because most legacy databases do not have clean schema documentation. You need visibility into field names, relationships, and data types before you can map anything. SchemaSpy gives you just enough to start tracing, but most of the logic still comes from someone familiar with the actual application. ETL tools show up once the mapping is complete. At this point, the tools depend on environment: AWS DMS, Google Cloud DMS, and Azure Data Factory get used in cloud-first migrations.AWS Schema Conversion Tool (SCT) helps when moving from Oracle or SQL Server to something modern and open. On-prem, SSIS still hangs around, especially when the dev team is already invested in it. In custom environments, SQL scripts do most of the heavy lifting — especially for field-level reconciliation and row-by-row validation. The tooling is functional, but it’s always tuned by hand. Governance tooling Platforms like Atlan promote unified control planes: metadata, access control, policy enforcement, all in one place. In theory, they give you a single view of governance. In practice, most companies have to bolt it on during migration, not before. That’s where the idea of a metadata lake house shows up: a consolidated view of lineage, transformations, and access rules. It is useful, especially in complex environments, but only works if maintained. Gartner’s guidance around embedded automation (for tagging, quality rules, and access controls) shows up in some projects, but not most. You can automate governance, but someone still has to define what that means. Migration engines Migration engines control ETL flows, validate datasets, and give a dashboard view for real-time status and reconciliation. That kind of tooling matters when you are moving billions of rows under audit conditions. AWS DMS and SCT show up more frequently in vendor-neutral projects, not because they are better, but because they support continuous replication, schema conversion, and zero-downtime scenarios. Google Cloud DMS and Azure Data Factory offer the same thing, just tied to their respective platforms. If real-time sync is required, in trickle or parallel strategies, then Change Data Capture tooling is added. Some use database-native CDC. Others build their own with Kafka, Debezium, or internal pipelines. Most validation is scripted. Most reconciliation is manual Even in well-funded migrations, reconciliation rarely comes from off-the-shelf tools. Companies use hash checks, row counts, and custom SQL joins to verify that data landed correctly. In some cases, database migration companies build hundreds of reconciliation reports to validate a billion-record migration. No generic tool gives you that level of coverage out of the box. Database migration vendors use internal frameworks. Their platforms support full validation and reconciliation tracking and their case studies cite reduced manual effort. Their approach is clearly script-heavy, format-flexible (CSV, XML, direct DB), and aimed at minimizing downtime.  The rest of the stack is coordination, not execution. During cutover, you are using Teams, Slack, Jira, Google Docs, and RAID logs in a shared folder. The runbook sits in Confluence or SharePoint. Monitoring dashboards are built on Prometheus, Datadog, or whatever the organization already uses.  What a Serious Database Migration Vendor Brings (If They’re Worth Paying) They ask the ugly questions upfront Before anyone moves a byte, they ask, What breaks if this fails? Who owns the schema? Which downstream systems are undocumented? Do you actually know where all your PII is? A real vendor runs a substance check first. If someone starts the engagement with "don’t worry, we’ve done this before," you’re already in danger. They design the process around risk, not speed You’re not migrating a blog. You’re moving financial records, customer identities, and possibly compliance exposure. A real firm will: Propose phased migration options, not a heroic "big bang" timeline Recommend dual-run validation where it matters Build rollback plans that actually work Push for pre-migration rehearsal, not just “test in staging and pray” They don’t promise zero downtime. They promise known risks with planned controls. They own the ETL, schema mapping, and data validation logic Real migration firms write: Custom ETL scripts for edge cases (because tools alone never cover 100%) Schema adapters when the target system doesn’t match the source Data validation logic - checksums, record counts, field-level audits They will not assume your data is clean. They will find and tell you when it’s not - and they’ll tell you what that means downstream. They build the runbooks, playbooks, and sanity checks This includes: What to do if latency spikes mid-transfer What to monitor during cutover How to trace a single transaction if someone can’t find it post-migration A go/no-go checklist the night before switch The good ones build a real migration ops guide, not a pretty deck with arrows and logos, but a document people use at 2AM. They deal with vendors, tools, and infrastructure, so you don’t have to They don’t just say "we’ll use AWS DMS." They provision it, configure it, test it, monitor it, and throw it away clean. If your organization is multi-cloud or has compliance constraints (data residency, encryption keys, etc.), they don’t guess; they pull the policies and build around them. They talk to your compliance team like adults Real vendors know: What GDPR, SOX, PCI actually require How to write access logs that hold up in an audit How to handle staging data without breaking laws How to prepare regulator notification packets if needed They bring technical project managers who can speak of "risk", not just "schema." So, What You’re Really Hiring You’re not hiring engineers to move data. You’re hiring process maturity, disaster recovery modeling, DevOps with guardrails and legal fluency. With 20+ years of database development and modernization expertise, Belitsoft owns the full technical execution of your migration - from building custom ETL pipelines to validating every transformation across formats and platforms. Contact our experts to get a secure transition, uninterrupted operations, and a future-proof data foundation aligned with the highest regulatory standards.