Predictive Financial Analytics for Proactive Risk Management: A Machine Learning Framework for Early Detection of Banking Vulnerabilities

Abstract

The banking industry is expanding and the financial risk environment is rapidly shifting amidst digital transformation, and proactive risk management is needed. Traditional risk assessment approaches are based on historic analysis and reactive decision making, and are not suited for identifying new vulnerabilities early on. This study aims to present a predictive financial analytics framework using machine learning techniques to improve the early detection of banking vulnerabilities and improve the risk management capabilities. The study examines the possibility of combining financial transaction data with operational measures and risk factors to build intelligent models that can detect unusual patterns, forecast risks, and aid in decision making for regulators. The structure is structured to increase the accuracy and timeliness of financial risk detection using cutting edge analytical methods like data preprocessing, feature engineering, predictive modeling and performance evaluation. The algorithms used in machine learning are analyzed for their ability to categorize risk scenarios, uncover patterns in intricate financial data, and deliver actionable insights for banking institutions.