[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126566-en":3,"doc-seo-126566-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126566,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Machine learning due diligence evaluation to increase NPLs profitability transactions on secondary market","This paper advances non-performing loans (NPLs) profitability analysis on the secondary market by proposing a machine learning-based due diligence framework. A loan is treated as non-performing when repayment likelihood is low, and an ad hoc dependent random forest regressor is used to project recovery rates for secured NPL portfolios. Transaction profitability is modeled through forecasts of expected net repayments and related collection times. The evaluation aims to mitigate the “lemon discount” from information asymmetry between banks and investors, especially for higher-quality collateralised NPLs.","Review of Managerial Science  \n[https://doi.org/10.1007/s1](https://doi.org/10.1007/s1)1846-023-00635-y  \nORIGINAL PAPER  \nMachine learning due diligence evaluation to increase NPLs profitability transactions on secondary market  \nMaria Carannante1 · Valeria D’Amato1 · Paola Fersini2 · Salvatore Forte3 · Giuseppe Melisi4  \nReceived: 25 May 2022 / Accepted: 27 February 2023 © The Author(s) 2023  \nAbstract  \nIn this paper, we contribute to the topic of the non-performing loans (NPLs) business profitability on the secondary market by developing machine learning-based due diligence. In particular, a loan became non-performing when the borrower is unlikely to pay, and we use the ability of the ML algorithms to model complex relationships between predictors and outcome variables, we set up an ad hoc dependent random forest regressor algorithm for projecting the recovery rate of a portfolio of the secured NPLs. Indeed the profitability of the transactions under consideration depends on forecast models of the amount of net repayments expected from receivables and related collection times. Finally, the evaluation approach we provide helps to reduce the ”lemon discount” by pricing the risky component of informational asymmetry between better-informed banks and potential investors in particular for higher quality, collateralised NPLs.  \nKeywords NPLs · Machine learning · Due diligence · OVERALS Mathematics Subject Classification 91G15 · 91G70  \n1 Introduction  \nAccording to Banca Ifis report 2021/2022, “even though in 2020 Italy still has a Non-Performing Exposures (NPE) ratio above the EU average, we expect European NPE stock to increase by 60 billion euros in 2022–2023, worse than that estimated for the Italian financial system”. From 2017 to 2020, over 50 billion euros were  \nMaria Carannante, Valeria D’Amato, Paola Fersini, Salvatore Forte, and Giuseppe Melisi have contributed equally to this work.  \n* Maria Carannante[mcarannante@unisa.it](mcarannante@unisa.it)  \nExtended author information available on the last page of the article  \n1 3  \nFig. 1 Source: Ifis Bank NPLs Market Database-News and press releases-Banca Ifis internal analysis 2021  \ninvested in the Non-Performing Loans (NPLs) market to buy approximately 214 billion euros of NPLs portfolios.  \nNevertheless, the intense transactional activity on the secondary market - the incidence of 32% in 2021 (IFIS Banca (2022)) -determined the dynamism of the NPLs market. For instance, based on the annual report (Osservatorio Nazionale NPE Market (2020)) by the National Observatory NPE Market of Credit Village (Italy), the Italian secondary market recorded a significant boom as regards 2020:  \n284 with 12 billion in euros of Gross Book Value (GBV) . The main highlight of the survey relies on advanced maturity degree, after a chaotic triennium from 2017 to 2019, which has been relevant to the contribution of foreign investors in the divestiture of whole portfolios.  \nThe market shows deals divided between secured and unsecured loans, with a high incidence of corporate customers, with a high component of an unsecured portfolio. The market shows a sort of normalization with a greater balance between the types of portfolios transacted. On the contrary, the first years of growth of the NPLs transaction market were characterised by a high concentration on Unsecured credit portfolios (Fig. 1) .  \nThe transfer of the NPLs to the secondary market can represent a de-risking strategy for the banks operating in the primary market since it relaxes the burden on the NPLs management. Indeed it could help banks offload NPLs from their balance sheets and distribute the risk, free up bank resources, and strengthen bank stability. In other words, the transactions on the secondary market appear an attractive de-risking strategy, due to the high flexibility characterising the securitization structure. In particular, the new legal framework, the EU Directive (EU) 2021/2167 on credit servicers and credit pur","cbCaipmkhFr4MaUm","https://ap.wps.com/l/cbCaipmkhFr4MaUm","pdf",1022086,5,1,21,"English","en",105,"# Abstract\n# Keywords\n# 1 Introduction\n## NPL market background and secondary market activity\n## Legal framework and implications for secondary NPLs markets\n## Focus on secured NPLs and profitability drivers","[{\"question\":\"What does the paper propose for NPL secondary-market due diligence?\",\"answer\":\"It develops a machine learning due diligence framework that uses a dependent random forest regressor to project recovery rates for a portfolio of secured NPLs.\"},{\"question\":\"How is profitability linked to the modeling approach?\",\"answer\":\"Profitability depends on forecast models for net repayments expected from receivables and the collection times required to obtain them.\"},{\"question\":\"What issue does the approach aim to reduce in pricing transactions?\",\"answer\":\"It helps reduce the “lemon discount” by pricing the risky component arising from information asymmetry between better-informed banks and potential investors, particularly for higher-quality collateralised NPLs.\"}]","Machine learning due diligence evaluation to increase NPLs profitability transactions on secondary market | 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