Explainable AI for Retail Futures & Options Risk Classification: An Applied Case Study Using Real NSE Data

Abstract

This paper presents an applied case study in which a Random Forest classifier is trained on real NSE Futures & Options data to flag option contracts as high-risk or low-risk, using four established risk dimensions — implied volatility, leverage, days to expiry, and liquidity - with SHAP used to make each prediction interpretable at the trade level. Drawing on 160,826 real option-contract-day observations, the classifier achieved perfect classification performance, discussed transparently as a consequence of the rule-based labelling method rather than independent predictive discovery. The contribution is methodological: demonstrating a reproducible pipeline connecting real exchange data, transparent risk labelling, and trade-level explanation, positioned as a decision-support concept for retail traders.