\r\nThus, a method for the practical use of statistical and machine learning approaches in the estimation of expected loss has been established. The results will be used to help develop credit risk research, improve the sophistication of banks\u2019 loan screening, streamline financial administration, and facilitate small and medium-sized enterprise finance<\/strong>.\r\n<\/p>\r\n\r\n
Project Background<\/h4>\r\n
\r\nBanks play an important role in indirect finance. Since financial crisis, they have been given the urgent task of creating a more effective their risk management measures than the existing one. In addition to the lending risk faced by banks, the estimated loss of the companies in the lender’s portfolio is a problem. The factors in expected loss can be categorized as Probability of Default (PD) and Loss Given Default (LGD). The estimate of LGD, particularly, is an important indicator for the BASEL II Advanced Internal Ratings-Based Approach (new BIS regulations) and when calculating loan loss reserves under International Financial Reporting Standards (IFRS). However, as a result of the limited integrated database, only a few methods have been proposed for the LGD estimation (Chart-1).\r\n<\/p>\r\n \r\nAs evidenced by the 2008 financial crisis, more elaborate risk management by banks is required for the stability of finance and economics in society. The risk held by banks can be broadly divided into three types: credit risk, market risk, and operational risk. The greatest risk out of these three is credit risk, and among credit risk, risk on lending is known to be the major risk faced by banks. \r\nThe following subthemes are being elucidated to build an optimal estimation model using statistical models and machine learning in the integrated database.\r\n<\/p>\r\n \r\nWe are performing a comprehensive review on cutting edge statistical models and machine learning model based estimation accuracy standards.\r\n<\/p>\r\n \r\nWe are performing a comprehensive review on cutting edge statistical models and machine learning model based estimation accuracy standards.\r\n<\/p>\r\n \r\nTo find the optimal variables for a model, we are using variable selection methods based on mathematical statistics, methods checked from the operator\u2019s perspective, and comprehensive and exhaustive methods that fully utilize computational resources during the exploration process.\r\n<\/p>\r\n \r\nWe are using methods such as least squares estimation, maximum likelihood estimation, and Bayesian estimation to obtain the optimal parameters.\r\n<\/p>","protected":false},"excerpt":{"rendered":"Project Summary and Anticipated Outcomes and Goals This study deals with the estimation of expected loss using […]","protected":false},"author":1,"featured_media":355,"parent":905,"menu_order":20,"comment_status":"closed","ping_status":"closed","template":"page-research-third.php","meta":{"_acf_changed":false,"_locale":"en_US","_original_post":"https:\/\/csds.iscw.jp\/wp\/?page_id=108","footnotes":""},"class_list":["post-909","page","type-page","status-publish","has-post-thumbnail","hentry","en-US"],"acf":[],"aioseo_notices":[],"_links":{"self":[{"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/pages\/909","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/comments?post=909"}],"version-history":[{"count":1,"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/pages\/909\/revisions"}],"predecessor-version":[{"id":910,"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/pages\/909\/revisions\/910"}],"up":[{"embeddable":true,"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/pages\/905"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/media\/355"}],"wp:attachment":[{"href":"https:\/\/csds.rois.ac.jp\/wp-json\/wp\/v2\/media?parent=909"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}![\"\"](\"..\/..\/..\/..\/images\/omd_03.png\")
\r\n<\/figure>\r\n\r\n\r\n\r\nProject Research and Development Content<\/h4>\r\n
\r\nLending risk is mainly supplemented by the expected loss of the lender. This can be broadly divided as (1) Probability of Default (PD), and (2) Loss Given Default (LGD). PD is the probability that a lender will default, that is, that they will fail to make repayment within a certain period (usually a year).
\r\nLGD describes the amount of loans outstanding at the time of default as a ratio of the actual loss of the defaulting lender. Banks should make precise estimates of these factors for accurately assessing the risk of loan assets.
\r\nBasel II is a new type of regulation on capital adequacy that aims to maintain the soundness of internationally operating banks. It states that credit risk should be estimated using (A) a standardized method (via external ratings) and (B) an internal ratings method. The internal ratings method (B) can be classified as (B-1) \u201cFundamental Internal Ratings-Based Approach,\u201d which estimates only the PD, and (B-2) \u201cAdvanced Internal Ratings-Based Approach,\u201d which estimates both the PD and LGD.
\r\nHowever, the lack of an integrated database due to the highly confidential nature of bank data poses difficulties in estimating the credit risk of bank assets using the aforementioned method. In particular, the number of databases for LGD estimation, including (1) “actual loan collections” (figures on actual collections, that is, incurred losses by banks through their lending), which is essential for LGD analysis, is severely lacking.
\r\nFurthermore, the practical use of highly accurate LGD estimation methods requires (2) the inclusion of variables that are considered effective, such as \u201ccollateral\u201d and \u201cguarantees,\u201d and (3) the existence of a database that can integrate data from multiple banks so that the results are not bank-specific. The Synthesis Credit Risk Database Consortium was built for the purpose of creating a single database for evaluation of credit risk in Japan that satisfies conditions (1) through (3). The Synthesis Credit Risk Database of five banks was created for the estimation of expected loss using this research.\r\n<\/p>\r\n(a) Compliance with BASEL II\/IFRS<\/h5>\r\n
(b) Model with the highest estimation accuracy<\/h5>\r\n
(ii) Comprehensive review of cutting-edge models and machine learning models<\/h5>\r\n
(d) Optimal parameters for the model<\/h5>\r\n