From Reactive Losses to Forward Foresight: India's Quest for Robust Credit Risk Assessment

By IndraStra Business News Desk

Cover Image Attribute: Image by Pabitra Kaity from Pixabay

 Within the complex framework of India’s financial system, credit risk assessment stands as a quiet yet pivotal guardian, determining whether loans fuel growth or fester into burdens. The consumer products sector, often called the "heartbeat of everyday life" because it supplies food, clothing, and home essentials to millions, offers a vivid lens into this process. Companies here face economic pressures that ripple through balance sheets, testing banks' ability to discern viable borrowers from those teetering on the brink of default. Yet, as India's economy expands—its digital lending market projected to reach $515 billion by 2030—the tools and frameworks for evaluating these risks reveal persistent gaps. Public and private sector banks alike score below satisfactory levels in credit risk management, with performance indices hovering at 49 percent and 47 percent, respectively, pointing to deficiencies in training, data availability, and technology adoption. This reality demands a measured examination of how India might evolve its practices, drawing on regulatory shifts toward expected credit loss models and international standards like IFRS 9, while confronting the biases embedded in emerging algorithmic approaches.
The foundations of credit risk assessment in India rest on a system that categorizes assets as standard or non-performing based on fixed rules, a method that incorporates conservatism and active supervision to preserve stability. This backward-looking approach examines financial statements, cash flows, and collateral to gauge a borrower's capacity to repay, often through the classic "5 Cs": capacity via debt service coverage ratios ideally at least 1.2, capital invested by owners (typically 30 percent of project costs), collateral or guarantees, conditions of the loan, and character inferred from credit history. Such evaluations aim to identify risks in lending situations, draw conclusions on payment likelihood, and structure loans accordingly. However, this framework struggles in dynamic markets. The 2018 IL&FS crisis, in which distress in an established firm evaded early detection, exposed these limitations, much as the global financial crisis revealed miscalculations in assets such as mortgage-backed securities. In India, credit risk remains the primary threat to banks, tied directly to rising non-performing loans that erode profitability and capital.
Historical data from the consumer products sector highlights these vulnerabilities. Between 2019 and 2022, over 1,000 public companies in Africa, the Middle East, Asia, and India grappled with financial strain, marked by spikes in median probability of default values, particularly in 2019 and 2020. Indian firms showed a sharper rise than peers in the Middle East and North Africa, driven by reduced consumer spending, supply chain disruptions, and intensified competition. By 2022, recovery emerged through government stimulus and demand rebound, yet the episode illustrates how broader economic shocks amplify sector-specific woes. Liquidity challenges, where debt outpaces revenue, and operational inefficiencies further compound issues, as seen in cases where sales downturns prevent creditor repayments, leading to insolvency.
Consider Sintex Industries Ltd., a Gujarat-based manufacturer of plastics and textiles founded in 1982. From early 2017 to mid-2018, its credit score declined steadily, with early warning signals turning amber in July 2017 and red by April 2018, flagging heightened default risk. The COVID-19 onset in mid-2020 accelerated this drop, though dynamic thresholds in monitoring tools helped distinguish firm-specific distress from pandemic-wide pressures. Factors like mounting debt, eroding market share from domestic and international rivals, and supply chain bottlenecks culminated in proceedings under insolvency laws. This trajectory, tracked through evolving credit scores and stock price declines, reveals how unchecked pressures can cascade into systemic concerns, emphasizing the need for vigilant, real-time oversight in credit decisions.
India's banking sector, resilient in aggregate with sufficient capital to weather moderate macro-financial stresses, has nonetheless borne the scars of past episodes. The non-banking financial intermediary space has diversified but grown more interconnected, heightening contagion risks. Access to credit for underserved groups—such as those in informal sectors—remains hampered by weak legal, tax, and informational infrastructures for asset-based and digital lending. Emerging threats like cybersecurity gaps and climate-related exposures add layers of complexity; while risks from climate change seem containable, they require finer data granularity for mapping. The Financial Inclusion Index climbed to 64.2 by March 2024 from 43.4 in 2017, bolstered by initiatives like Pradhan Mantri Jan-Dhan Yojana (PMJDY), which opened over 548.4 million accounts holding Rs 2.45 trillion. Yet, these gains coexist with rising non-performing loans in unsecured personal loans and credit cards, where short-term profitability masks longer-term vulnerabilities.
Against this backdrop, the Reserve Bank of India (RBI) has pursued reforms to align with global norms. The IMF's Financial System Stability Assessment, revisited in recent analyses, urges adoption of IFRS 9 to fortify credit risk management, including sharper supervision of individual loans, collateral valuation, and connected borrower groups. This standard shifts provisioning from incurred losses to expected ones, incorporating forward-looking macroeconomic forecasts. The RBI affirms its dedication: “India remains committed to the adoption of internationally accepted standards and best practices in a phased manner, attuned to domestic needs and economic conditions wherever necessary.” Such a transition promises to compel banks toward deeper analysis of credit exposures, fostering disclosures on internal models, scenarios, and risk profiles that benefit investors, analysts, and depositors weary of opaque executive narratives.
Central to this evolution is the Expected Credit Loss (ECL) framework, which the RBI plans to introduce imminently, with a 12-month rollout. Unlike the current regime, ECL mandates lifetime loss forecasts for loans, adjusting provisions dynamically to economic shifts, asset quality changes, and evolving threats. “The promise of ECL is real. Banks will be compelled to do a lot more analysis and modelling of their credit risk, to create provisions. Also very importantly, banks will need to disclose much more about their internal risk models, expected scenarios, and the credit risk in those situations,” as one analysis frames it. This could prove transformative, equipping banks with tools akin to advanced radar in a fighter jet, allowing anticipation of turbulence rather than mere reaction. For sectors like consumer products, where median default probabilities fluctuated wildly during the pandemic, ECL might enable earlier adjustments in pricing, capital allocation, and risk strategies, potentially curbing the kind of distress that felled Sintex.
Yet, implementation carries weighty hurdles. “India significantly trails behind international best practices when it comes to credit risk modelling,” a candid assessment notes, rooted in the complexity of predictive modeling where real-world events often outstrip data-driven forecasts. Banks must pour resources into technology, analytics, and expertise—a burden heavier for smaller institutions than their larger counterparts, who may adapt with relative ease. Biases in judgment, particularly in high-growth areas like unsecured lending, could delay risk recognition, while inherent model limitations persist. The shift demands not just regulatory tweaks but a cultural pivot in how banks approach credit, from reactive provisioning to proactive scenario planning. As one observer puts it, “This could be transformative for investors, analysts, and depositors, who have often had to rely on vague executive statements or incomplete data.”
Parallel to these structural changes, algorithmic credit risk assessment emerges as both an accelerator and a cautionary tale. Fintechs leverage artificial intelligence and machine learning to sift through vast datasets—income records, transaction histories, even browsing and social media patterns—for real-time borrower evaluations. This supplants manual processes, curbing errors and subjectivity while drawing on large datasets for sharper predictions. In India, where 1.4 billion adults remain unbanked globally but digital footprints proliferate, such tools hold promise for inclusion. Platforms like Fundfina use transactional data for micro, small, and medium enterprises (MSMEs) facing a $5 trillion global credit gap, while KarmaLife analyzes gig-economy earnings and ratings, matching the predictive power of traditional bureau data. Rental and utility payments, once overlooked, now render previously invisible borrowers scorable, boosting approval rates and reducing losses.
The COVID-19 era amplified this trend, with lenders turning to geolocation, satellite imagery, and mobility indices to parse "false bads"—borrowers flagged as risky due to temporary shocks—from genuine defaulters. In South Africa, such misclassifications jumped from 1.5 percent pre-pandemic to 8 percent by October 2020, prompting alternative data's rise to avert credit rationing. Remittances, totaling $794 billion globally in 2022 with $626 billion to low- and middle-income countries, serve as collateral in models like Nepal's Laxmi Bank, extending loans to migrants. In India, the Account Aggregator framework facilitates consent-based sharing of financial data for underwriting, while 2022 Digital Lending Guidelines mandate necessity-driven collection and transparent audits, prohibiting access to contact lists or call logs beyond know-your-customer needs.
Despite these advances, algorithmic tools introduce fresh perils. Models trained on biased historical data can perpetuate discrimination, arbitrarily denying credit based on proxies for protected traits like socioeconomic status. The "black box" nature obscures decision rationales, eroding trust and complicating bias detection. Privacy invasions loom large, as personal data from e-commerce or social media risks breaches leading to identity theft or harassment. “The integration of alternative data in credit risk assessment brings forth several opportunities, challenges, and risks across different dimensions,” capturing the duality. In India, the RBI's 2025 Digital Lending Directions consolidate rules on partnerships, reporting, and grievances, requiring assessments via age, occupation, and income factors. Yet, they sidestep AI-specific mandates, creating a vacuum: “Paragraph 7 mandates REs to assess creditworthiness based on factors like age, occupation, and income, and ensures credit limits do not increase without explicit requests.” This oversight ignores the shift from manual to automated methods, leaving gaps in auditing, transparency, and equity.
International precedents offer blueprints for bridging these divides. The European Union's AI Act classifies credit risk algorithms as high-risk, demanding risk management systems, human oversight, accuracy benchmarks, and data governance. In the United States, the Consumer Financial Protection Bureau's Circular 2022-03 holds creditors accountable for decisions, even opaque ones, under acts like the Fair Credit Reporting Act and Equal Credit Opportunity Act. Singapore's FEAT Principles advocate fairness through justifiability and accuracy, ethics via minimum standards, accountability in oversight, and transparency in communication. India could draw from these, perhaps via a joint working group to draft conduct codes or inter-agency ties under the Digital Personal Data Protection Act. As one critique urges, entities must furnish rationales for denials, lest lenders "hide behind algorithms."
Within banks, entrenched practices compound these issues. Employees receive insufficient training to grasp credit risk conceptually or handle it adeptly, while information technology's potential for administration remains underutilized, especially in public sector banks. Comprehensive data for evaluations falls short, and advanced techniques like Altman's Z-score or CreditMetrics find scant use, despite Basel II's emphasis on modeling for capital adequacy. “More popular credit evaluation techniques like Altman’s Z score model, J.P. Morgan credit matrix, Zeta analysis do not find a place in the credit evaluation tool kit of the commercial banks in India,” a study laments. “The availability of comprehensive data for credit evaluation is far from satisfactory in commercial banks in India.” Such shortcomings inflate capital through delayed loss recognition, fostering moral hazard and perpetuating non-performing assets.
The ECL framework's arrival, intertwined with IFRS 9, could address these by enforcing forward-looking provisions that integrate alternative data's granularity. Transactional records from telcos or e-commerce might enhance models by 5 to 25 percent, as seen in Equifax's UK trials, while open banking initiatives like India's expand data pools ethically. For MSMEs and gig workers, this means bridging gaps—53 million U.S. consumers gain visibility through rental data alone, with average scores of 631—potentially elevating India's inclusion to levels rivaling Colombia's 97 percent potential. Yet, the digital divide persists: women and minorities, with slimmer footprints, risk exclusion unless sex-disaggregated data informs designs.
Smaller banks face acute strains in this transition, potentially skimping on investments and amplifying systemic risks. Larger entities, partnering with fintechs like Tonik Bank in the Philippines or FCMB in Nigeria, may thrive, but equity demands support for laggards through sandboxes or no-action letters, as in the U.S. or Indonesia. Regulatory blacklists for biased proxies, alongside industry codes for ethics, could safeguard against discrimination, while digitizing government data—taxes, electricity patterns—builds reliable inputs. Cross-border remittance flows would aid migrant-heavy economies, and literacy campaigns would empower consumers in consent processes.
In essence, India's credit risk landscape teeters between stagnation and renewal. The consumer sector's pulses—its defaults and recoveries—mirror broader banking frailties, from Sintex's unraveling to algorithmic biases in fintech lending. ECL and IFRS 9 offer a scaffold for maturity, compelling transparency and foresight that elude the current order. “The test of ECL modelling will be whether it helps banks recognise these risks at the right time and adjust their provisioning, capital management, pricing, and risk management strategies accordingly.” Reforms must proceed phased, attuned to domestic contours, bolstering training, data, and oversight to harness innovations without courting inequities. For a financial system that has withstood pandemics and crises, this evolution holds not fanfare but quiet imperative: a resilient apparatus, forged in balance, to sustain the everyday lives it underwrites. The journey ahead, as one voice concludes, is “promising—but far from easy.” 

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