Business Intelligence and Machine Learning as the Infrequent Tool for Risk Analysis in Financial Sector: A Review of Related Literature

Abstract

Business intelligence and machine learning can improve risk management models. Business intelligence and machine learning use diverse data kinds to predict probable events and evaluate risk losses. Business applications are influenced by BI, ML, and other algorithmic applications. Modern corporate management is influenced greatly by technology. They offer company management solutions, including financial sector risk management. Business intelligence and machine learning in financial services risk management are understudied. While credit risks were studied extensively, liquidity, market, and operational concerns were neglected. Risk management in financial services has grown in the recent decade. Previously, banks detected, measured, and reported threats. They now use business intelligence and machine learning to improve risk management. This article studied how BI and ML can be used in banking risk management. Business intelligence and machine learning deliver better risk management results than traditional statistical models. To complete this work, the researcher reviewed the literature on business intelligence and machine learning in financial risk management. The researcher uncovered industry and academic research on financial services advancements, specifically risk management. BI and ML indicate enhancing financial sector risk management, however, some areas need more research. The research advised studying BI and ML models for financial sector risks. It analyzed and evaluated machine-learning risk management strategies. It recognized risk management issues and suggested solutions.