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Fintech for Startups
Playbooks and case studies covering fintech for startups.
GST Compliance Cheatsheet for SaaS Products Serving Indian SMBs
A reference sheet for SaaS founders whose billing engine treats GST as one flat tax field. Covers the four axes GST branches on — and the invoice fields that make or break your customers' input tax credit claims.
How GimBooks Scaled GST Filing to 3M Users Without a DBA
GimBooks handled normal load fine but crashed every GST deadline cycle. Here's the architectural call that fixed it — and why horizontal scaling alone would have made the problem worse.
What Is Idempotency? Stop Charging Customers Twice
A double-charge after a mobile timeout is almost always a retry bug, not a payment gateway bug. Here's how idempotency keys work, and why the fix lives in your client — not your server.
How GimBooks Served 3M Users Without Breaking GST Logic
A walkthrough of how the GimBooks accounting SaaS handled GST edge cases at scale by treating compliance as a state machine, not a calculation library. The lesson generalizes to any fintech whose rule logic works at 50K users but silently breaks at 500K.
How GimBooks Kept AI Accurate Across 3M Downloads
When an AI bookkeeping feature works at 10K users but breaks at 500K, the instinct is to blame data volume. The GimBooks case study shows the real culprit is usually segment collapse — and the fix is architectural, not statistical.
Feature Stores Explained: Why Your AI Keeps Training on Lies
Your credit-scoring model passes every offline test, then degrades two weeks after deployment. The culprit isn't drift — it's that your training pipeline and your serving pipeline are computing features differently, and no one is enforcing they match.
How KarroFin Scored 250K Users Without a Credit Bureau
KarroFin's underwriting model was rejecting creditworthy borrowers for the wrong reason: absence of bureau data. Here's the engineering call that fixed it, and why chasing the bureau score is the wrong target for any lender serving thin-file users.
Your AI Feature Has a Trust Problem, Not an Accuracy Problem
Your model is 92% accurate. Your acceptance rate is 11%. The fix is not a better model. The fix is making the output legible at the moment a user has to act on it.
Questions to Ask Before Hiring an AI Fintech Dev Partner
Most AI fintech vendors demo well and use the right words. These 15 questions separate the ones who have actually shipped under regulatory and credit-risk constraints from the ones who haven't.
How KarroFin Scaled AI Credit Scoring Without Killing Approval Rates
KarroFin's credit model wasn't broken. No alerts, no errors, no engineering fires. But approval rates were quietly compressing at scale — and the fix wasn't where the data science team was looking.
Feature Stores Explained: Why Your ML Models Stale Out
Your credit risk model nailed backtesting but production accuracy keeps slipping. The culprit is rarely the model — it's a silent mismatch between how features are computed at training time and at inference. Here's what a feature store actually does about it.
5 Mistakes We See Teams Make Shipping AI to Thin-File Users
Most thin-file AI lending models don't fail because the architecture is wrong. They fail because the team never audited what happens after the first batch of rejections starts retraining the model. Here are the five failure modes we see most often.