Fintel AnalyticsEvent-Driven Data Architecture for Fintech: Build It Right in 2026
Most fintechs collect event data but fail to operationalise it. Here's how to build an event-driven data architecture that gives you real-time intelligence — without the chaos.
Fintel Analytics blog
Data & AI Insights
Expert perspectives on data analytics, AI agents, internal tools, and business intelligence from the Fintel Analytics team.
54 of 206 posts
Alternative Data Analytics for Fintech: Build the Edge in 2026
Alternative data analytics gives fintech startups a real intelligence edge — but only if your data pipeline can actually ingest, clean, and model it at speed. Here's how to build it right.
Partner Revenue Analytics for Fintech: Build Real Visibility
Fintech platforms with partner ecosystems rarely have clean visibility into how revenue is split, earned, and reconciled. Here is how to fix that with a proper data stack.
RegTech Data Pipelines: Automate Compliance Workflows in 2026
Manual compliance is costing fintechs more than they realise — in headcount, fines, and failed audits. Here is how to build the data infrastructure that fixes it.
Intraday Liquidity Analytics for Fintech: Build Real Visibility
Most fintech treasury teams are managing intraday liquidity from delayed reports and spreadsheets. Here is how to build the data stack that gives you real-time visibility — and why it matters more than ever in 2026.
Loan Portfolio Analytics for Fintech Lenders: Build It Right in 2026
Most fintech lenders drown in loan data but lack a single trusted view of portfolio performance. Here's how to build the analytics stack that actually works.
Stablecoin Payments Analytics: Build the Data Stack That Works
Stablecoin payment volumes are exploding — but the data infrastructure most fintechs are running is not built for on-chain flows. Here is how to fix that.
Open Banking Data Analytics: Turn API Feeds Into Intelligence
Open banking API feeds generate enormous volumes of raw transaction data — but most fintech teams lack the analytics layer to turn it into decisions. Here's how to fix that.
Credit Risk Analytics for Fintech Lenders: Build It Right in 2026
Most fintech lenders don't have a modelling problem — they have a data infrastructure problem. Here's how to build the analytics stack that credit risk decisions actually depend on.
Fraud Analytics for Fintech: Fix False Positives in 2026
Fraud analytics for fintech payments is about more than catching bad transactions — it is about building the data infrastructure that stops your fraud model from killing legitimate revenue.
PSP Data Analytics: Build Intelligence Into Your Payment Stack
Most fintechs track whether payments succeed or fail — but few build analytics that reveal why. This guide shows how to unlock real intelligence from your PSP data layer.
Regulatory Reporting Analytics for Fintech: Build It Right in 2026
Manual regulatory reporting is costing fintech teams time, money, and regulatory goodwill. Here is how to build analytics infrastructure that makes compliance a data engineering problem — and solves it permanently.
Treasury Analytics for Fintech Startups: Build Real Visibility in 2026
Most fintech startups fly blind on treasury until it costs them. Learn how to build real liquidity visibility with data engineering and BI — before the gap becomes a crisis.
API Analytics for Fintech: Turn Your API Layer Into a BI Asset
Your APIs process every payment, every user action, every integration event — but most fintech teams never analyse that data. Here is how to fix that.
BigQuery Column-Level Encryption in dbt: Protect Sensitive Data
BigQuery column-level encryption lets you protect PII and financial data at the field level — but wiring it correctly into dbt without breaking downstream models is harder than the docs suggest.
Usage-Based Billing Analytics: Fix Your Revenue Data in 2026
Usage-based billing creates complex revenue data that breaks most standard analytics stacks. Here is how to build the data layer that actually works.
Why Your Data Stack Breaks Under VC Due Diligence (And How to Fix It Before Your Series A)
Most early-stage data stacks look fine until a VC opens a data room request. Here's what breaks, why it costs founders term sheets, and how to fix it.
Fintech Ledger Analytics: Fix Broken Balance Intelligence in 2026
Most fintechs have a ledger. Very few have analytics built on top of it. This guide shows exactly what breaks, and how to fix it.
FX Exposure Analytics for Fintech: Stop Flying Blind on Currency Risk
Most fintech startups have no real-time visibility into their FX exposure — only spreadsheets and gut feel. Here's how to fix that before it costs you.
Virtual IBAN Analytics: Fix Broken vIBAN Data in 2026
Virtual IBANs promise automated reconciliation — but the data stack behind them is usually broken. Here is how to fix it before it costs you.
Settlement Timing Analytics: Fix Your Post-Trade Data Stack
Settlement timing analytics tell you exactly where your post-trade process breaks down — before regulators or counterparties do. Here is how to build the data stack that survives T+1.
Dispute Operations Analytics for Fintech: Build It Right in 2026
Most fintech dispute teams are drowning in volume with no analytics stack to manage it. Here is how to build one that actually works.
Network Token Analytics: Fix Your Payment Performance Data
Network tokens promise higher auth rates and lower fraud — but most fintechs can't measure whether they're delivering. Here's how to build the analytics layer that proves it.
Neobank Analytics: Build the Data Stack That Scales in 2026
Most neobanks scale their product long before they scale their data. Here's how to build the analytics stack that actually supports growth, profitability, and investor scrutiny.
Acquirer Analytics: Build the Data Stack That Wins in 2026
Most acquirers are flying blind on the metrics that matter most. Here is how to build the internal analytics stack that turns transaction data into competitive edge.
Fintech Burn Rate Analytics: Build Real Visibility in 2026
Most fintechs track burn rate in a spreadsheet — until it breaks. Here's how to build a real analytics stack that gives finance and leadership actual visibility.
Crypto Exchange Analytics: Build the Data Stack That Scales
Most crypto exchanges generate enormous volumes of data but operate with surprisingly immature internal analytics. Here's how to fix that — from fee revenue modelling to liquidity dashboards.
KYC/AML Compliance Analytics for Fintech: Build It Right in 2026
Most fintechs have a KYC vendor. Very few have a compliance analytics layer behind it. Here is how to build one that actually works in 2026.
Digital Wallet Analytics: Fix Broken Balance & Transaction Data in 2026
Digital wallet analytics breaks when transaction data isn't modelled correctly. Learn how to build a reliable, reconcilable analytics layer on top of your wallet data.
Investor Due Diligence Analytics: Build a Data Stack That Survives the Process
When investors open your data room, what they find — or fail to find — can make or break your round. Here is how to build an analytics stack that holds up under scrutiny.
Spend Analytics for Finance Teams: Fix Your Data in 2026
Most finance teams have spend management tools — but still can't answer basic questions about where money is going. Here's why, and how to fix it.
BaaS Analytics: Build the Data Stack Behind Embedded Banking
Most BaaS-powered products ship fast but build no analytics layer. Here's how to fix that — and what it costs you if you don't.
Revenue-Based Financing Analytics: Build the Data Stack That Scales
Revenue-based financing is growing fast — but most RBF platforms are flying blind on portfolio risk, repayment performance, and unit economics. Here is how to fix that.
Multi-Currency Analytics: Fix Broken Financials in 2026
Multi-currency analytics is one of the most underengineered problems in fintech data stacks. This guide shows how to fix it before it costs you real money.
Payments Reconciliation Analytics: Automate & Scale in 2026
Manual payments reconciliation breaks at scale — and the cost is higher than most fintech teams realise. Here is how to automate it properly with modern data engineering.
Payment Gateway Routing Analytics: Fix Your Routing Blind Spots
Most fintechs run multi-gateway stacks but have no analytics layer to tell them whether their routing logic is working. Here's how to fix that.
Payment Operations Analytics: Fix Blind Spots in 2026
Most fintech ops teams fly blind on the metrics that matter most — settlement timing, PSP cost efficiency, and reconciliation breaks. Here is how to fix that with a proper payment operations analytics layer.
Chargeback Dispute Analytics: Stop Flying Blind on Losses
Chargebacks are no longer just a cost of doing business — they are a data problem. Here is how to build the analytics stack that turns dispute losses into managed, measurable outcomes.
Interchange Fee Analytics: Stop Flying Blind on Card Costs
Most fintech and payments businesses are losing basis points on every transaction without knowing it. Here is how to build interchange fee analytics that actually show you where the money is going.
Merchant Portfolio Analytics: Build Risk & Revenue Intelligence
Most payment facilitators are sitting on a goldmine of merchant data and doing almost nothing with it. Here is how to fix that — and what it is worth when you do.
Revenue Leakage Analytics: Fix Hidden Losses in 2026
SaaS and fintech companies silently lose 3–5% of ARR to revenue leakage every year. Here's how to build the analytics infrastructure that finds and fixes it.
Card Programme Analytics: Build Intelligence Into Your Issuing Stack
Most fintech card issuers are flying blind. They know their card is live — but they don't know why authorisation rates dropped, which spend categories drive interchange, or which cardholders are drifting to a competitor.
BNPL Analytics: Build Risk & Performance Intelligence in 2026
BNPL platforms generate complex, high-velocity data that most early-stage teams are not equipped to model. Here is how to build the analytics infrastructure that actually scales.
Cross-Border Payments Analytics: Build Real Corridor Visibility
Most cross-border fintechs are flying blind on their most important metrics. Here's how to build the analytics layer that gives you real corridor visibility.
Financial Close Analytics: Cut Your Month-End Close in 2026
Half of finance teams still take six or more days to close the books. Financial close analytics fixes the broken data pipelines causing that delay — here is how to build it.
Cash Flow Forecasting Analytics: Fix Bad Numbers in 2026
Most cash flow forecasts are wrong — not because of bad assumptions, but because of broken data infrastructure. Here is how to fix it.
BigQuery Access Control: Row & Column Security Done Right
Most growth-stage companies running BigQuery give everyone too much access. Here is how to implement row-level and column-level security that scales with your team.
Webhook Ingestion Analytics: Stop Losing Events in Your Pipeline
Webhook events are failing silently — and your dashboards are lying to you because of it. Learn how to build reliable webhook ingestion analytics that protect revenue and power trusted metrics.
Authorization Rate Analytics: Fix Declining Payments in 2026
Poor authorization rates silently drain revenue from payment businesses. This guide shows exactly how to build the analytics stack to diagnose, monitor, and fix them.
Embedded Finance Analytics: Build Intelligence Into Your Platform
Embedding financial products into your platform is one thing. Measuring whether they're working — and why — is another problem entirely. Here's how to build the analytics stack your embedded finance operation actually needs.
Two-Sided Marketplace Analytics: Fix Broken Liquidity Metrics
Most marketplace founders are measuring the wrong things. Here is how to build analytics that actually reveal whether your platform is healthy — or quietly failing.
ML Feature Pipelines for Startups: Fix Training-Serving Skew in 2026
Training-serving skew is the silent killer of production ML. Learn how to build feature pipelines that actually hold up — and stop your models degrading the moment they ship.
ML Model Monitoring for Startups: Stop Your Models Degrading in Production
ML models don't break loudly — they decay quietly, and by the time you notice, the damage is done. Here's how to build production monitoring that actually works.
Cohort Analysis for Startups: Why Your Retention Numbers Are Lying to You
Aggregate retention metrics can mask serious churn problems at fast-growing companies. Here's how to build cohort analytics that surface the real story — and what to do when you don't like what you find.