Artificial intelligence is rapidly reshaping how lenders assess borrowers, moving credit underwriting away from rigid rule-based systems towards dynamic, behaviour-driven models. While traditional metrics such as credit scores and repayment history will not disappear overnight, their dominance is clearly weakening.
Historically, lending decisions relied on fixed criteria. Banks and NBFCs evaluated income, credit scores and past repayment behaviour; applicants who met these thresholds were approved, while others were rejected.
This approach, though efficient, has long excluded large sections of potential borrowers and often overlooked the nuances of real-world financial behaviour.
AI is now changing that. Instead of relying solely on static records, lenders can analyse how individuals actually manage money on a day-to-day basis.
The biggest shift is from rule-based evaluation to behaviour-based prediction.
AI-led underwriting models examine:
Bank transaction patterns
Spending habits
Cash flow consistency
For instance, a borrower with limited credit history but steady monthly income and disciplined spending may be rejected under traditional models. AI systems, however, can identify such patterns and classify the applicant as creditworthy.
Cash flow is emerging as a critical metric. Rather than static snapshots, lenders now focus on how money moves through an account. Stable income, regular savings and controlled expenses together signal a stronger borrower profile.
Another key development is the use of alternative data alongside conventional inputs. This includes:
Utility bill payments
Digital transactions
GST filings and small business activity
Telecom and e-commerce behaviour
Such data helps bring unserved and underserved borrowers into the formal credit system—particularly relevant in India, where a large share of the workforce operates in the informal sector.
AI also enables more granular risk assessment, allowing lenders to price loans differently based on individual profiles.
The Reserve Bank of India’s August 13, 2025 “Framework for Responsible and Ethical Enablement of Artificial Intelligence” (FREE-AI) outlines a structured approach to adopting AI in finance.
According to the report:
About 20.8 percent of surveyed entities—mainly large banks and NBFCs—are using or developing AI systems
Nearly 13.7 percent of these applications focus on credit underwriting
Key use cases include document verification, credit risk assessment and automated decision-making
The report also notes growing reliance on alternative data to assess “new-to-credit” borrowers, though challenges such as high costs, talent shortages, data quality issues and legal uncertainties persist—especially for smaller institutions.
The RBI has outlined core principles—often referred to as the ‘Seven Sutras’—to guide ethical AI deployment:
Transparency and secure systems
Human oversight in decision-making
Responsible and socially aware usage
Fairness and avoidance of bias
Accountability of institutions
Explainability of models
Safety, resilience and sustainability
These safeguards are critical. AI systems are only as good as the data they are trained on; biased inputs can lead to biased outcomes.
Credit underwriting is set to become increasingly dynamic. Instead of one-time assessments, lenders will continuously evaluate borrower behaviour across the loan life cycle, adjusting limits and offering support in real time.
The shift towards intelligent credit is not just a technological upgrade. It marks a transition towards a more inclusive, responsive and accurate lending ecosystem.
For lenders, the advantage lies in better risk management and wider customer reach. For borrowers—especially those outside the formal credit system—it could mean easier access to finance, even without a traditional credit score.
