ML-powered fraud screening for applicants with limited credit history. Combines alternative data signals — device fingerprint, behavioural biometrics, income proxy, address velocity — into a single risk score with explainability.

Upload income statements and spending patterns — the AI structures the financials, benchmarks against peer cohorts, generates a personalised offer with a recommended interest rate and APR, and runs a fully documented loan decision in under 60 seconds.

Real-time BNPL credit decisioning at checkout. Sets per-customer limits based on spending history and repayment behaviour, splits purchases into 3/6/12-month plans, and flags high-risk transactions before approval.

Design a dataset schema column by column — choose name, data type (numeric, categorical, date, boolean), and statistical distribution or fixed value set. Generate thousands of realistic rows for model training, testing, or regulatory demonstration without using real customer data.

Continuously score borrower risk using repayment behaviour and spending signals, auto-segment loans into structured tranches (AAA → Equity), bundle pools for asset-backed securities, and present each tranche to matching investor classes — all driven by ML with live portfolio monitoring.