The FSA published the English version of "FSA Analytical Notes (2025.6) vol.3 ". 
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As financial institutions’ business environments and profit structures change, it is important to understand economic and market trends based on data, and to accurately grasp the business conditions of individual financial institutions and also the resilience and vulnerabilities of the financial system as a whole. From this perspective, the FSA has been focusing on the utilization of granular data, such as transaction-level bank loan data and financial data on individual corporations. Some case examples of data analyses using such granular data are published as a series of reports titled “FSA Analytical Notes.”
This issue contains "Attempt to Identify Early Warning Signals on Credit Risks using Regional Banks’ Loan Data and Macroeconomic Indicators".
This paper presents a machine learning model designed to predict trends in credit risk by leveraging regional banks’ granular loan data combined with macroeconomic indicators, with the aim of detecting early warning signs potentially affecting the management of banks. The analysis targets downward transitions in borrower classifications and identifies indicators that appear useful for capturing such warning signs, as well as offering insights into the current economic environment compared to past crisis periods. With the full-scale launch of the Common Data Platform, we will keep on making efforts to expand the scope of analysis, refine indicator selection, enhance the model, and apply these findings in dialogues with financial institutions.
Enhancing the use of data in financial supervision and policy-making is a medium- to long-term agenda. The FSA will continue to build its data analysis capabilities and data infrastructure.
* Unless otherwise noted, the figures and tables in this report were prepared by the FSA.
