Welcome to Our Series of Webinars - Deep Dives with IIQF Experts
Indian Institute of Quantitative Finance
Program details
Session Coverage:
The transition from traditional econometrics to Machine Learning (ML) in market risk management is no longer a theoretical debate but an operational evolution.
While traditional models like Value at Risk (VaR) and Expected Shortfall (ES) remain the regulatory bedrock—with ES gaining prominence under the Fundamental Review of the Trading Book (FRTB)—ML architectures such as Random Forests, LSTMs, and XGBoost are demonstrating a superior ability to map the non-linear, "fat-tailed" risks that define modern market crises. However, "outperformance" is a multi-dimensional metric.
While ML wins on predictive precision, traditional models maintain a significant lead in regulatory transparency and structural stability.
- This webinar explores the tension between these two paradigms and the emerging "Quant-ML" synthesis.
Speaker: Ritesh Chandra
CFA, MBA from IIM Calcutta and B Tech from IIT Kanpur. He has also earned the Sustainability and Climate Risk (SCR) certification from GARP.
His diverse industry experience includes leadership roles at Yes Bank, RBL Bank and Barclays Bank, where he successfully managed large credit portfolios and teams.
Ritesh is an accomplished trainer and educator with 2,000+ hours of training sessions delivered over the last 10 years. With 15+ years experience as a banker specializing in in Credit Risk, Trade Finance and Project Finance, he brings a wealth of practical insights and hands-on-experience to his training programs. He is a member of the IMT-Center for Distance Learning, Ghaziabad Board of Studies
Presently, he works as a freelance trainer / educator with various organizations including Indian Institute of Quantitative Finance (IIQF), College of Supervisors, Reserve Bank of India, Emeritus Program Leader for courses in AI/ML, CRISIL Academy Trainer for Credit Risk, Great Learning teaches a course on Digital Banking and Finance and for many other educational institutes.
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