Custom Investment Management and Copy Trading Software with a CRM for a Broker Company

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.

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.