AI In Loan Decisions: Why Human Oversight Is Now Critical In India

AI-driven underwriting is spreading across Indian lending. The bigger shift now is continuous human checks so faster approvals do not turn into scaled-up wrong calls.

Digital Loans Get Faster, But Oversight Is Getting Tighter

India’s digital credit engine is getting more automated, with lenders leaning on models to screen customers, price risk, and flag fraud. The upside is speed and reach. The risk is silent error at scale. 

If the data pattern shifts, or the model learns the wrong proxy, rejections rise for the wrong cohort, and delinquencies creep up without a clear reason. Recent moves by large players show the momentum. For example, Reuters reported on 23/02/2026 that Bharti Airtel plans ₹200 billion investment to expand digital lending capabilities.

When Algorithms Decide, What Is The Issue?

The core issue is not AI scoring itself, but unchecked automation. Models can drift as borrower behaviour changes, and teams may still treat outputs as final. 

That leads to 2 risks. First, credit losses rise if the model keeps approving weak profiles. Second, genuine borrowers get rejected when the model over-corrects. The impact is amplified in small-ticket, high-volume lending where decisions are rapid and repeatable. Industry data shows how concentrated this segment has become.

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Before the deeper story, here is the scale snapshot lenders are reacting to.
 

This growth is exactly why human-AI teamwork is becoming a frontline control, not a compliance afterthought.

How Human-AI Teamwork Can Improve Loan Decisions?

The practical playbook now is “automation for routine, humans for risk.” Lenders are redesigning underwriting into 3 layers. First, AI handles straight-through approvals only when confidence is high and the profile fits familiar patterns. 

Second, a human review gate kicks in for edge cases: thin-file customers, irregular income, policy exceptions, or conflicting signals. Third, post-decision monitoring checks if the model is behaving differently from last month.

The monitoring piece is where outcomes improve. Teams track approval-rate swings by segment, early delinquency signals, and stability of rejection reasons. A sharp rise in overrides becomes a warning that the model is out of tune. KPMG’s note on the responsible AI framework highlights that automation can reduce human error but can also “amplify faults at scale,” and flags model drift and third-party risks.

What Happened Earlier? The Build-Up Behind “Keep Watching The Model”?

The warnings around algorithm-heavy lending have been building for a while, especially for NBFC-led growth segments. Reuters reported on 16/05/2024 that the regulator cautioned non-bank lenders against growing reliance on algorithm-based credit models and flagged risks of aggressive lending in certain segments. 

That caution was later echoed in mainstream business coverage in India, including a report dated 16/05/2024 highlighting similar concerns in the NBFC context.

More recently, the conversation shifted from warning to structured governance. Reuters reported on 13/08/2025 about a committee-backed AI framework for finance with 26 recommendations across governance, protection and assurance, and a proposal for a standing committee to keep assessing risks. 

International trade press also covered this direction on 21/08/2025, noting the push for alternative data use and inclusion, but alongside risk controls.

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Now the focus is operational: daily checks, not annual audits.
 

Human review gate

Overrides, exception rationale, supporting documents, and turnaround time

Post-decision monitoring

Drift signals, approval-rate shifts, early delinquency trends, complaints and dispute outcomes 

These controls help lenders protect portfolio quality and reduce avoidable rejections.

Stakeholders Weigh In: What Different Sides Want?

Lenders want faster approvals and lower costs, but risk teams want documented overrides and clear audit trails. 

Borrowers want understandable rejection reasons and a review route. Vendors push automated tools, while internal audit teams ask for monitoring evidence.