Financial Risk Management in Indonesian Banking: The Integrative Role of Data Analytics and Predictive Algorithms

Abstract

This research delves into the realm of financial risk management within the Indonesian banking sector, with a focus on leveraging Data Analytics and Predictive Algorithms. Amidst the global financial market's complexities and the evolving nature of banking risks, this study aims to provide a comprehensive understanding of how advanced technological tools can enhance risk identification, evaluation, and management. Utilizing extensive datasets from the Indonesian Banking Statistics, Central Statistics Agency, and Bank Indonesia, the research explores the intricate relationship between various banking risks and macroeconomic factors. The study employs sophisticated predictive models to analyze data, focusing on credit and operational risks. The findings highlight the significant impact of macroeconomic variables on banking risks and the effectiveness of predictive models in risk assessment. The research contributes to the existing literature by offering a detailed analysis of the integration of machine learning and big data analytics in banking risk management. It also provides strategic insights for banks to adopt more dynamic, data-driven risk management strategies in the face of economic and industrial changes. The study underlines the importance of continuous innovation in technological applications to meet the evolving demands of the banking sector.