The Indian Financial system from 1997 to 2026 using Prospect Theory and the Black Swan Theory

Abstract

Financial systems are not mechanical structures that simply transmit savings into investment. They are social and institutional systems in which people repeatedly make decisions with incomplete information. This paper examines how uncertainty is interpreted through heuristics, how those interpretations interact with financial institutions, and how the resulting feedback loops can convert a relatively small disturbance into system-wide stress. The analysis combines the heuristics-and-biases framework of Tversky and Kahneman, prospect theory, Taleb's distinction between fragility, robustness and antifragility, and a Black Swan lens concerned with rare, high-impact events and the limits of prediction.
India provides a useful case because the country experienced a sequence of overlapping shocks rather than a single isolated crisis. The 2018–20 shadow-banking episode followed years of credit expansion, infrastructure lending, stressed assets and growing dependence on non-bank intermediaries. The defaults of IL&FS and DHFL then triggered uncertainty, flight to safety, funding withdrawal and a contraction in credit. COVID-19 subsequently arrived while the financial system was still repairing these weaknesses. The paper argues that the transmission of these shocks cannot be understood through liquidity or balance-sheet variables alone. Representativeness, availability, anchoring and loss aversion provide plausible behavioural mechanisms through which uncertainty was transformed into collective retrenchment, while institutional design determined how far that retrenchment could spread.
A descriptive empirical assessment reinforces this interpretation. Domestic financial intermediation deepened substantially between 1997 and 2022, while bank asset quality subsequently improved markedly. By March 2026, scheduled commercial banks reported a GNPA ratio of about 1.8 percent and a CRAR of about 17.7 percent, while RBI stress tests continued to place aggregate capital above regulatory minima under severe hypothetical shocks. These developments support a conclusion of considerable robustness and resilience. However, the evidence is not sufficient to classify the entire system as antifragile. Antifragility requires not merely surviving a shock, but demonstrably learning from volatility in ways that reduce future fragility