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The next test for India’s credit system and how AI fits in
Rising inflation, rising credit demand: Can AI manage the risk?
India has weathered many global headwinds in the past, including wars and tariff disruptions. The strong foundations of India’s economic policies have helped the economy remain resilient through such events. However, no country today is immune to geopolitical shocks, especially one as large and interconnected as India.
Global shocks and their ripple effects on India
Such shocks can create ripple effects not only at the macroeconomic level but also for individuals and businesses. Exports may be affected, particularly at a time when private investment is already subdued. The rising importance of critical imports, especially crude oil, can translate into higher inflation in non-discretionary items.For a country like India, which imports a significant portion of its crude oil requirements, sustained increases in global oil prices can directly affect fuel costs, transportation, and eventually food and household expenses. All of this creates a cascading effect from inflationary pressure on the common person.
While some discretionary expenses, such as international leisure travel, may be postponed, most essential expenses will continue. As the impact of inflation becomes more evident, both individuals and businesses may increasingly rely on credit to manage cash flows.
Why underwriting and risk management matter more now
The resilience of the financial system fundamentally depends on strong underwriting practices, i.e. identifying risk early, predicting potential defaults, and implementing proactive risk mitigation strategies. Today, AI-ML-led risk models used by lenders can enable faster implementation of new strategies.However, the fuel for these systems remains clean, accurate, and comprehensive data. While alternative data sources are expanding, credit bureaus, which hold over a decade of lending history for many borrowers, remain highly effective.
Bureau data can play a critical role in revising and upgrading risk models to address unforeseen risks. Over the past year, lenders have generally maintained strong risk controls across portfolios, keeping bad debts contained and delinquencies range-bound across segments.
The real test of underwriting and portfolio management strategies lies ahead. Microeconomic pressures that affect borrowers’ disposable incomes must not be allowed to deteriorate loan portfolio quality, particularly among new borrowers. It is essential to differentiate between resilient borrowers and those who may be over-leveraged and vulnerable during difficult times.
In this fast-evolving credit landscape, portfolio monitoring is no longer just about tracking who has defaulted; it is about identifying the stress building in the portfolio before it becomes a crisis.
Credit bureaus enable lenders to do exactly this by turning vast volumes of credit data into predictive indicators. A regular, intelligent health check of their entire loan book is as necessary as risk underwriting at the onboarding stage. Banks, NBFCs and fintechs can monitor how their borrowers are behaving not only with them, but across the broader financial system, highlighting behaviour such as increasing credit utilisation and exposure to new loans being taken elsewhere, or any instability being observed in loan repayments.
The role of AI and bureau data
This is even more powerful with the integration of AI and machine learning capabilities within bureau solutions. These models do not just report what has happened, they analyse patterns, learn from historical behaviour, and help predict which accounts may show early signs of stress. Instead of manually sifting through data, lenders receive borrower segment-based insights and highlight customer segments that need proactive engagement, which geographies are seeing risk build-up, and which profiles are resilient.Rather than reacting after defaults occur, lenders can adjust credit limits, recalibrate underwriting norms, and fine-tune collection strategies in advance. This results in not only better risk management, but also onboarding of a better segment of borrowers, and stronger portfolio management. Thus, bureau data enhanced by AI and ML becomes forward-looking business intelligence for profitable lending.
In the past, lenders have successfully implemented robust portfolio management strategies using bureau data to enable proactive customer engagement. Early warning systems and real-time signals will now become even more critical in monitoring portfolio health. Systemic resilience is key to absorbing such microeconomic shocks and emerging stronger, supported by the robust foundational policies that underpin India’s lending-led economic growth.
Disclaimer: The information provided in this article is for informational purposes only and does not constitute financial, legal, or professional advice. While every effort has been made to ensure accuracy, readers should verify details independently and consult relevant professionals before making financial decisions. The views expressed are based on current industry trends and regulatory frameworks, which may change over time. Neither the author nor the publisher is responsible for any decisions based on this content.
Sachin Seth, Regional Managing Director, CRIF India & South Asia
Source: LiveMint
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