Regulations fintech products must account for
In India, the RBI's digital lending guidelines require loans to be disbursed directly into the borrower's bank account and repaid directly to the lender, with a key fact statement showing the full cost of credit and disclosure of every lending service provider involved. Payment aggregators need RBI authorization, payment system data must be stored in India under the RBI's localization rules, and onboarding must follow the KYC Master Direction, which permits video-based customer identification.
Card data brings PCI DSS obligations, and products serving Europe face PSD2 strong customer authentication and the GDPR. Rules change often, so architecture should make consent screens, disclosures, limits and partner configurations editable without new releases. This is general information, not legal advice; your compliance team or counsel should confirm what applies to your license and partners.
- Store key fact statements and consent records for every loan.
- Keep payment system data on servers located in India.
- Log every partner API request and response for audit.
- Separate customer funds from operating money in the ledger design.
- Make limits, fees and disclosures configurable by compliance staff.
- Version every policy document customers accept, so you can show what they agreed to.
Build, partner or license: choosing your stack
Few fintech startups hold every license themselves. Most partner with a bank or regulated NBFC for lending or accounts, use payment gateways such as Razorpay, Cashfree or Stripe for collections, and call specialist APIs for KYC, credit bureaus, bank statement analysis and account aggregator data. Partnering gets you to market faster, while owning more of the stack gives control over margins and customer experience later.
The architecture should keep partners swappable. Put each external provider behind your own internal interface, keep the ledger and customer records in your system, and store every request and response for audit. When a partner changes pricing, fails an audit or goes down, you can switch without rewriting the product.
Plan for scale in the ledger from the start. A double-entry ledger with immutable entries, idempotent transaction APIs and daily reconciliation against bank and gateway files prevents the silent mismatches that become painful at higher volumes and during audits. Fixing ledger design after launch is expensive.
Where AI fits in fintech
Common uses include fraud and anomaly detection on transactions, credit models that add alternative data to bureau scores, collections prioritization, document extraction from bank statements and salary slips, and support assistants that answer account questions. Each needs explainable decisions, fairness testing across customer groups, human review for adverse outcomes and monitoring, because regulators and customers will ask why a loan was declined or a payment blocked.
Start with problems where data already exists and outcomes appear quickly, such as transaction fraud or document extraction. Credit models need longer validation, because loan performance takes months to observe, so run them in shadow mode alongside existing rules before letting them influence approvals.
