Regulatory expectations for bank technology
Indian banks and NBFCs work under RBI directions that shape how software is built and run. The Master Direction on IT governance, risk, controls and assurance practices sets expectations for IT strategy, change management, access control and audits. The Master Direction on outsourcing of IT services makes the regulated entity responsible for its vendors, including audit rights and data access. Cyber incidents must also be reported to CERT-In within six hours.
In practice that means vendors must support information security audits and vulnerability assessments, keep payment data in India, provide documentation, follow change approval processes and give the bank's auditors access when asked. This is general information, not legal advice; your compliance team will map the exact obligations for your entity type. Budget time for these steps in every release plan.
Plan for these requirements in the architecture rather than in a final checklist. Role-based access with maker-checker approvals, immutable audit logs, encrypted data stores, separate environments for development and production, and documented change management make audits faster and reduce the risk of findings that delay a launch.
How to choose a banking software partner
Banking projects reward partners who respect controls rather than work around them. A good partner asks early about your security policies, maker-checker rules, audit requirements and core banking interfaces, and plans testing with your UAT and audit teams instead of treating them as a final hurdle. The questions below help separate experienced teams from generalists.
Also check how the partner works with your internal IT and vendor management teams. Banking programs involve security reviews, procurement and steering committees, and a partner used to this rhythm plans for it instead of being surprised by it. Ask for examples.
- Which core banking or loan systems have you integrated with before?
- How do you handle maker-checker approval and audit trails?
- Can you deploy in our data center or private cloud?
- How do you respond to IS audit and VAPT findings?
- What happens to the code and documentation if the contract ends?
- How do you document architecture and controls for our auditors?
Where AI fits in banking
Banks gain the most from AI in document-heavy and alert-heavy work. Models can extract data from loan applications, bank statements and property papers, prioritize anti-money laundering alerts for investigators, predict which overdue accounts will respond to which collection approach, and help relationship managers prepare for customer meetings. Each use needs explainable outputs, human sign-off for decisions affecting customers and model governance that auditors can review. These use cases also produce clear before and after metrics.
Start where errors are cheap to catch and value is easy to measure, such as document extraction reviewed by staff or alert prioritization that still leaves the final call to an investigator. Track accuracy and time saved, then expand to decisions with more customer impact once governance is proven.
