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.
Fintel Analytics blog
Expert perspectives on data analytics, AI agents, internal tools, and business intelligence from the Fintel Analytics team.
32 of 206 posts
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.
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.
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.
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.
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.
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.
Virtual IBANs promise automated reconciliation — but the data stack behind them is usually broken. Here is how to fix it before it costs you.
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.
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.
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.
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.
Knowing when to graduate from spreadsheets to a data warehouse is one of the most consequential infrastructure calls a founder makes. Here is the framework we use.
Usage-based billing creates complex revenue data that breaks most standard analytics stacks. Here is how to build the data layer that actually works.
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.
Most loyalty programmes generate plenty of data and almost no usable insight. Here is how to build the analytics stack that actually proves ROI and drives decisions.
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.
Most scaling e-commerce brands track ROAS religiously but can't explain why profit shrinks as revenue grows. Here's how to build the analytics stack that fixes that.
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.
Most B2B SaaS companies reach Series A with their core growth metrics — ARR, NRR, churn — still living in spreadsheets. Here is how to fix that before it costs you.
SELECT * in dbt BigQuery models is one of the quietest cost drivers in a growing data stack. Here's how to find every offending model and fix it.
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.
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.
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.
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.
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.
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.
Parsing nested JSON incorrectly in dbt BigQuery staging models is one of the most common — and most expensive — anti-patterns in modern analytics stacks. Here is how to find and fix it.
Most fintech dispute teams are drowning in volume with no analytics stack to manage it. Here is how to build one that actually works.
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.
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.
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.
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.