RISK MANAGEMENT IN INVESTMENT BANKING

Abstract

Risk management is at the heart of investment banking, where institutions face a spectrum of risks including market risk, credit risk, liquidity risk, and operational risk. Traditional risk management relies heavily on Value at Risk (VaR), stress testing, scenario analysis, and expert judgment to measure, monitor, and mitigate these exposures. However, these methods often assume linear relationships and static volatility, which may fail to capture realworld complexities and sudden market shocks.This study combines conventional risk management frameworks with advanced analytics. Using Machine Learning models like Random Forest and XGBoost, we aim to detect hidden, nonlinear risk factors and predict potential losses more accurately. Additionally, Deep Learning models such as LSTM networks are employed to forecast time-series risk metrics like daily VaR and liquidity ratios, effectively capturing volatility clustering and long-term dependencies in financial data.By integrating ML and DL into risk analysis, the study demonstrates significant improvements in predictive accuracy and early warning capabilities, offering investment banks a more dynamic and datadriven approach to risk management.