A succinct synopsis of predictive analytics for fraud detection and credit scoring in BFSI, - Latha Narayanan Valli (CV. Shofanah Media Berkah, 2024)
The Banking, Financial Services, and Insurance (BFSI) industry is undergoing a change thanks to predictive analytics, which uses statistical methods and machine learning algorithms to predict future trends and probability. An overview of the main advantages, anticipated developments, difficulties, and factors to be taken into account with predictive analytics in BFSI are given in this abstract. Improved risk management, better decision-making, more customer satisfaction, and operational efficiency are some of the main advantages. Future trends include improvements in real-time processing capabilities, growing usage of big data and IoT, and developments in AI and machine learning. Ensuring data quality and regulatory compliance are challenges, while ethical data use and model interpretability are problems. To fully realize predictive analytics' potential in BFSI, success in the field necessitates resolving obstacles, embracing emerging trends, and maintaining moral principles.