Development of a Practically Applicable Estimation Method for Expected Loss Ratio of SMEs: Using Integrated Big Data of Regional Banks

2022 Fiscal Year Final Research Report

• Project/Area Number: 20K13581
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 07080:Business administration-related
• Research Institution: The Institute of Statistical Mathematics
• Principal Investigator: Nagahata Hideaki 統計数理研究所, リスク解析戦略研究センター, 外来研究員 (00815128)
• Project Period (FY): 2020-04-01 – 2023-03-31
• Keywords: 信用リスク / 機械学習 / データ構造化 / データ結合 / デフォルト時損失率 / ビックデータ

Outline of Final Research Achievements

Using the database for estimating loan loss ratio (LGD) at default, we aimed to realize data structuring that would contribute to improving the accuracy of LGD estimation. Specifically, we structured the data to include the time point t before and t after the default point. The data structure was confirmed to be effective for estimation by learning statistical models and machine learning, since it can incorporate default-related information more deeply and extensively in order to improve estimation accuracy. We believe that we have made clear progress not only in improving the accuracy of LGD estimation through the sophistication of statistical models and machine learning, but also as a result of our review from the database used for estimation, which pivoted to focus on improving estimation accuracy and gaining new knowledge.

Free Research Field

信用リスク

Academic Significance and Societal Importance of the Research Achievements

本研究は一般では公開されていない担保・保証・債務回収の情報を含む、地銀５行統合データベースを用いた期待損失率推計を扱い、汎用的で高精度な推計手法を開発・提案します。
これによって、期待損失率推計に対し統計的・機械学習的接近法を用いた推計手法が確立・実務利用され、その成果は信用リスク研究の発展、銀行の融資審査の高度化、金融行政の合理化、中小企業金融の円滑化に貢献することを目指します。