Business Intelligence Consultant for Fintech

Every segment of fintech in the U.S., from digital banking and payments to lending, insurance, wealth management, and crypto, relies on Business Intelligence to compete and operate efficiently. BI consultants turn huge datasets into dashboards, reports, and predictive models that drive day-to-day decisions and long-term strategy. Whether it’s a neobank analyzing onboarding funnels or a crypto exchange monitoring trade flows, the ability to extract insights from data is a differentiator. This is why fintech firms invest in BI functions and talent: data-driven decision making helps them innovate faster, serve customers better, manage risks, and achieve better performance in a highly competitive industry. Each segment uses BI in unique ways (appropriate to their domain), but all share a common theme: leverage data or fall behind. Hiring skilled BI professionals, or consultants who can advise on BI strategy, gives fintech companies the tools to harness data as a strategic asset. 

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.

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, but 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 and 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 or 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 through 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.