{"id":23316,"date":"2026-06-10T11:47:23","date_gmt":"2026-06-10T11:47:23","guid":{"rendered":"https:\/\/engineerbabu.com\/blog\/?p=23316"},"modified":"2026-06-10T11:47:23","modified_gmt":"2026-06-10T11:47:23","slug":"ai-in-fintech-software-development","status":"publish","type":"post","link":"https:\/\/engineerbabu.com\/blog\/ai-in-fintech-software-development\/","title":{"rendered":"AI in Fintech Software Development"},"content":{"rendered":"

In 2019, a salary advance platform came to the team with a problem that seemed simple.<\/span><\/p>\n

They had 50,000 salaried employees as borrowers. Most had bureau scores. Approval rates on bureau-only decisioning were acceptable. But 30% of their target market had no bureau history, first-time borrowers, recently employed, new-to-credit. The bureau said decline. The platform’s instinct said approve. The data wasn’t there to back the instinct.<\/span><\/p>\n

The team’s CTO had spent 17 years at Wishfin, building credit infrastructure across India’s largest credit marketplace. The pattern was familiar. India has over 400 million working adults. Roughly 40% have no meaningful credit bureau history. Traditional credit scoring leaves them out entirely.<\/span><\/p>\n

The solution wasn’t a better bureau integration. It was a different data layer entirely.<\/span><\/p>\n

The team built an <\/span>ML model<\/span><\/a> trained on salary credit patterns, bank statement transaction features, UPI transaction velocity, employer stability signals, and behavioural data from the application flow itself. The model approved thin-file borrowers the bureau would have declined and predicted their repayment behaviour with 91% accuracy at 90-day DPD.<\/span><\/p>\n

That platform became EarlySalary. It has disbursed over \u20b910,000 crore.<\/span><\/p>\n

That credit model, refined across millions of live disbursements, is the production AI the team brings to every fintech build. Not a proof-of-concept. Not a research model. Production code trained on real borrower data, validated against real repayment outcomes, deployed in a system processing real money.<\/span><\/p>\n

The Google AI Accelerator 2024 selection, top 20 globally, reflects specifically this capability: production ML for financial decisions at scale.<\/span><\/p>\n

This guide is about how AI transforms fintech and what building it actually requires.<\/span><\/p>\n

Email <\/b>mayank@engineerbabu.com<\/b><\/a> to scope your fintech AI build.<\/b><\/p>\n

\"\"<\/p>\n

The 6 AI Applications That Define Fintech in 2026<\/b><\/h2>\n

1. Alternative Data Credit Scoring<\/b><\/h3>\n

Traditional <\/span>credit scoring<\/span><\/a> uses bureau data: repayment history, credit utilisation, account age, inquiry count. This works for borrowers with established credit histories. It fails for approximately 1.7 billion adults globally who lack sufficient bureau history for a score.<\/span><\/p>\n

Alternative data credit scoring uses signals beyond the bureau:<\/span><\/p>\n

Bank statement features<\/b>: Income stability (variance in monthly credit inflows), salary regularity (same employer credit on predictable dates), expense discipline (fixed obligation ratio from debit patterns), cash buffer (average minimum balance), bounce frequency (returned debits as stress signal).<\/span><\/p>\n

GST transaction data<\/b>: For self-employed and MSME borrowers, GST return history provides verified revenue signals. Turnover trends, filing regularity, and buyer quality are extractable from GST data via the GSTN API. Between April and December 2026, PSBs sanctioned over \u20b952,300 crore in MSME loans using GST-based underwriting.<\/span><\/p>\n

UPI transaction history<\/b>: Payment behaviour at velocity: how many transactions per month, to what categories, with what regularity. A borrower making 150 UPI transactions per month with consistent utility and insurance payments is a different risk profile from one making 12.<\/span><\/p>\n

Telco data<\/b>: Recharge frequency, plan tier, roaming patterns. Used selectively for thin-file borrowers where other alternative signals are unavailable.<\/span><\/p>\n

Behavioural signals from the application itself<\/b>: How long the borrower spent on each form section, how many times they edited an income field, whether they copy-pasted versus typed their PAN number. These micro-behavioural signals, invisible to the borrower, correlate with misrepresentation patterns at population level.<\/span><\/p>\n

The model architecture for production alternative credit scoring:<\/b><\/p>\n