With the constant diruptions in the economy stemmed from global market turbulence, technological changes, and shift toward a more sustainable way of life, understanding banking behavior become a priority to maintain financial stability. This study examines the credit allocation behavior of banks in Indonesia, influenced by economic conditions, regulatory frameworks, technological advancements, and sector-specific challenges. Bank credit plays a vital role in macroeconomic stability, and economic fluctuations impact banks procyclical credit behavior. The Indonesian banking sector faces complex pressures and sectoral risks, emphasizing the need for solid policies from Bank Indonesia to maintain financial system stability. This research addresses two main questions: how client relationships affect credit supply decisions and how structural changes such as interest rates, climate change, and cybersecurity influence bank behavior. Utilizing primary and secondary data as well as machine learning (ML) methods, the study reveals insights into credit supply practices in Indonesian banks and the potential of big data and ML for a detailed assessment of credit distribution patterns. The findings highlight the importance of stricter oversight, technological integration, and sector-specific strategies, especially for SMEs and high-risk sectors such as tourism and mining. The study emphasizes integrating green finance, RegTech, and SupTech to enhance banking sector resilience and align credit activities with sustainability goals. By applying these insights, Indonesia can create a stable credit environment, support economic growth, and ensure banks are prepared to manage evolving risks in the financial landscape.
Keywords: bank behavior, credit growth, credit supply, machine learning
