Q1. Could you begin by providing a brief overview of your professional background, specifically highlighting your expertise in the industry?

I am a mechanical engineer with a Diploma in Infrastructure Finance from IIT Madras and over 18 years of experience in the field of Banking, corporate, renewable energy, etc. Started banking career in an office with Andhra Bank, a Public Sector Bank, and worked with other organizations like Kotak Mahindra Bank, Central Bank of India, Axis Bank, HDFC Bank, Tata Capital Limited, Chemtrols Solar, and currently serve as Vice President – Credit & Risk at Alt Mobility, a leasing company specializing in electric vehicle finance backed by Shell International.

During my tenure, I have worked in various fields from retail loans to large corporate loans, especially in Infrastructure finance (Renewable energy, Steel) as a Credit analyst. Besides traditional banking, I have worked with Tata Capital Limited in the leasing department and was instrumental in setting up leasing sustainable finance.

My work experience is supported by professional qualifications, including CAIIB (IIBF), Certified Credit Analyst, Insolvency Professional (IBBI), MBA (Risk & Credit), Certified Forensic Accounting and Fraud Examiner, Chartered Engineer, and approved Independent Valuer—Immovable Property.
Expertise in Credit & Risk  evaluation, renewable energy, Retail Banking, and Corporate Insolvency.

Q2. What strategic priorities and technology investments should banking leaders focus on to transform credit risk management into a competitive advantage and build resilience against financial shocks and regulatory changes?

Indian Banking Credit assessment is mostly driven by traditional assessments; however, with the increasing impact of policies, the multilateral exposure of firms to risks, and cross-country relationships of individual firms, the Banking Sector is sustainable for larger credit defaults by counter parties.  As such, some of the suggestions to build up a strong Financial organisation are as follows:-

• Scenario Analysis and Stress Testing: Move beyond historical credit data to embrace scenario analysis and stress testing for macroeconomic, geopolitical, and climate risks.
• Early Warning Signal Detection: Build capabilities to detect early warning signals such as payment behavior, supply chain disruptions, and ESG-related events.
• Systematic Integration of Transition Risk: Transition risk needs to be systematically integrated into credit assessments as the government focuses on a low-carbon economy. Unlike physical risks, which may have a delayed effect, transition risks—arising from policy, technology, market, and social changes—can directly and rapidly impact financial performance. Embedding these in portfolio evaluations is essential for robust risk management.
• Climate Risk Sensitivity Analysis: Conduct sensitivity analysis on portfolios for both physical and transition climate risks, including nature-related risks. Study recent climate events (e.g., floods, avalanches, heatwaves) and avoid high-risk areas to mitigate cascading impacts on the portfolio.
• Capital Buffer for Climate Risks: Build additional capital beyond regulatory requirements for transition and physical risks, and avoid or reduce exposure to high carbon-emitting sectors such as cement and steel.

Q3. How is the increasing integration of environmental, social, and governance (ESG) factors reshaping credit risk assessment frameworks, and what impact does this have on loan pricing and portfolio diversification strategies?

Lenders and financiers who implement the integration of ESG in credit assessment are successfully able to rebalance their exposures toward electrification, renewables, grid, efficiency, and low-carbon materials, and away from assets with policy phase-out risk (e.g., sub-critical coal, ICE-only supply chains). Shift real assets away from high-hazard climate zones and prefer regions with stable policy frameworks and robust adaptation investments.
Given the inherent risks posed by climate change, lenders are increasingly proposing an additional climate risk premium of 60 to 100 basis points above existing loan pricing.

Q4. How are alternative data and AI-powered credit scoring models enhancing the accuracy and inclusivity of credit risk assessments across retail and corporate portfolios, and what are the main operational and ethical challenges banks face in adopting these technologies?

Traditional Dependency for Public Sector Banks and Old Private Banks
•    Banks primarily depend on CIBIL/Experian/Equifax scores, as well as audited financials
•    SME & Corporate Lending: Assessed mainly on balance sheets, collateral cover, past repayment track record, and promoter background
•    Limitation: Excludes large segments of first-time borrowers, such as gig workers, migrant laborers, and micro-entrepreneurs who lack a bureau history

NBFCs’ Adaptive Models
•    Unlike banks, many NBFCs and fintech lenders have pioneered alternative data-driven underwriting
•    Their AI credit models typically incorporate:

• Retail / Individual Borrowers
  • ​​Mobile usage & recharge patterns
  • Utility bill payments & rental payment history
  • Digital footprints (UPI, e-wallet, online shopping, OTT subscriptions)
  • Ride-hailing, food delivery, and e-commerce transaction histories
  • Psychometric tests & behavioral analytics (risk appetite, stability indicators)
• SME / MSME Borrowers​​​​​​​
  • ​​​​​​​GST filings & e-invoicing data
  • Bank statement analysis (cash flow cycles, seasonality, irregularities)
  • Supply chain relationships and receivables/payables analysis
  • Satellite imagery for agricultural loans (crop health, land use verification)
  • Geolocation and logistics data for MSMEs with distribution networks

Q5. Which sectors or borrower segments benefit most from alternative data inclusion, and where do AI models still struggle to provide reliable risk insights?

Retail and New-to-Credit Borrowers
Data from alternative sources and credit models driven by AI are demonstrating significant worth in enhancing access for borrower groups that have historically been neglected. Retail new-to-credit (NTC) individuals, including gig workers, self-employed professionals, and young workforce entrants, typically lack a bureau history and exhibit inconsistent income patterns. Alternative datasets, such as UPI transactions, mobile usage, and utility bill payments, offer dependable insights into their repayment ability.

MSMEs and Semi-Formal Enterprises
Micro, small, and medium enterprises (MSMEs), especially those functioning in the unregulated or semi-formal economy without verified financial statements, gain advantages from lenders utilizing GST filings, e-invoicing, transaction data from supply chains, and cash flow analysis through bank statements, which act as reliable indicators of creditworthiness—particularly in trading, small manufacturing, and service industries.

Agriculture and Rural Financing
In agriculture and rural financing, because farmers and borrowers in the agricultural sector often possess minimal or no formal credit history, satellite images, crop yield information, weather trends, and input purchasing records enhance credit evaluations and reduce dependence on collateral-oriented lending models. Ultimately, within the realm of consumer credit growth, the swift rise of Buy-Now-Pay-Later (BNPL), digital micro-lending, and small personal loans has been facilitated by leveraging digital footprints, enabling fintechs and NBFCs to underwrite loans faster and more precisely for e-commerce-focused customers

Limitations of AI-Driven Models
Despite the advantages of AI-driven models in credit evaluation, they face notable drawbacks, particularly due to the limitations and reliability of granular data at the geographical level. Models that rely heavily on recent transaction data risk overestimating stability during boom periods and underestimating stress during downturns, as they struggle to capture long credit cycles. Moreover, AI struggles to adequately account for long-term structural risks such as climate transition, policy and regulatory shocks, or disruptive technological changes, which require forward-looking judgment beyond historical or transactional patterns.

Q6. How are advanced credit risk management frameworks evolving in Indian PSU and private banks to address emerging non-performing asset (NPA) challenges while supporting sustainable loan growth? 

Larger banks have designated an Early Warning System in place to monitor the portfolios, real-time monitoring, and EWS dashboards that pull transaction-level data, GST/ e-invoicing, and portfolio signals to detect stress earlier. Lenders are actively recalibrating risk appetite statements, enforcing sectoral concentration limits, and embedding stress tests.

Q7. If you were an investor looking at companies within the space, what critical question would you pose to their senior management?

Method for identification of Risk, mitigations & adverse impacts for acceptance of risk. Portfolio summary based on geography (High to Low – Climate Risk area). Use of AI Data for the evaluation of new-to-bank customers without compromising credit quality.