[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119589-en":3,"doc-seo-119589-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119589,687197207919,"Theodora","https://ap-avatar.wpscdn.com/avatar/a000253d6f5f7c60be?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779446848396160552",8,"Research & Report","Predicting ESG Controversies in Banks Using Machine Learning Techniques - Research Article","Mistreating environmental, social, and governance (ESG) concerns creates serious organizational risks, with particularly strong consequences in banks. This open-access research studies ESG-related controversies (ESGC) by applying machine learning to an almost uncharted risk domain. Using a panel dataset of 140 banks across 2011–2020 and feature selection methods, the study evaluates which features most improve out-of-sample ESGC prediction, highlighting the governance–employees dynamics and offering implications for researchers, practitioners, and regulators.","Corporate Social Responsibility and Environmental Management  \nRESEARCH ARTICLE  OPEN ACCESS   \nPredicting ESG Controversies in Banks Using Machine Learning Techniques  \nAnna Rita Dipierro1  | Fernando Jimenéz Barrionuevo2 | Pierluigi Toma1   \n1Department of Economics and Management, University ofSalento, Lecce, Italy | 2Department of Information and Communications Technology, University of Murcia, Murcia, Spain  \nCorrespondence: Anna Rita Dipierro (annarita.dipierro@unisalento.it)  \nReceived: 21 May 2024 | Revised: 31 October 2024 | Accepted: 20 January 2025  \nKeywords: banks | controversies | ESG | governance | machine learning | risk  \nABSTRACT  \nMistreating environmental, social, and governance (ESG) concerns has serious drawbacks in organizations of any type, and even more in banks. Deeply revolutionized in its taxonomy of risks, banking sector is herein evaluated in its integration of ESG parameters that, when lacking, leads to ESG-related controversies (ESGC). Thereby, this research approaches the almost uncharted territory of ESGC in banks, by means of machine learning. Aiming at selecting the set of features that are relevant in ESGC prediction, techniques belonging to feature selection are used over a real panel dataset of 140 banks evaluated for a wide set of features over 2011–2020 time-span. We find the power that governance-employees dynamics detains in making out-of-sample predictions and forecasting of ESGC banks' risk. Finally, we provide implications for researchers, practitioners and regulators, further confirming the need for the rapid inroads that machine learning tools are actually making in the banking toolkit and in the regulatory technology.  \n1 | Introduction  \nIn adding more light toward the critical junctures of Machine Learning (ML)—a sub-branch of Artificial Intelligence (AI)—that economists and econometricians are, may and should be interested in, Chan and Mátyás (2022) seem providing us the lens to scrutinize ML in its ability to transform management decision. This work wants to answer the call for more light on the dynamic taxonomy of risks of banks, by means of ML. The ability to deal with large, and complex dataset and pursuing prediction tasks (Kinywamaghana and Steffen 2021) makes ML adequately mature do be deployed in the sector. The banking sector abounds of traditional econometric approaches, whereas there is a large room to apply ML tools, which are adequately mature do be deployed in. Actually, the European Money and Finance Forum (traced the rapid inroads that ML tools are making in the  \nbanks toolkit and in regulatory technology (Doerr, Gambacorta, and Serena 2021), becoming a research trend in the financial stability regulation (Chao et al. 2022) .  \nLed by the question of Hirsch (2018),“what then of the potential use of AI in reputation risk management?,” this work exploresan almost uncharted category of risks in banks, through a novel approach. We focus on scandals. The questionable behaviors of certain banks revealed a shady image of the sector (Nirino et al. 2021) . Scandals do abound coming form the financial accounting side (Pilkington 2022), as from the under-researched scandal named London Whale in 2012, from a group of traders that operated on account of JP Morgan Chase & Co. Also, disruptive scandals are emerging from the employees' side complaining unsustainable conditions, as in Goldman Sachs in 2013 and 2021, and Bank of America in 2013.  \n\n| Anna Rita Dipierro carried out this research during her Ph.D. at LUM University (Casamassima, BA, Italy) and her fellowship at University of Salento (Lecce, LE, Italy) . Anna Rita Dipierro is now affiliated to University of Calabria as a Post-Doctoral Researcher. She receives support within the GRINS project–Growing Resilient, INclusive and Sustainable from the European Union Next-Generation EU (GRINS PE00000018, CUP: H23C24000110006, Spoke 4 Sustainable Finance) . |\n| --- |\n| This is an open access article under the terms of the ","cbCaipu2FJKhNwrU","https://ap.wps.com/l/cbCaipu2FJKhNwrU","pdf",929763,1,20,"English","en",105,"# Introduction\n## ESGC and banking risk taxonomy\n## Machine learning approach and feature selection","[{\"question\":\"What is ESGC and why is it important for banks?\",\"answer\":\"ESGC refers to controversies arising from the mistreatment of environmental, social, and governance (ESG) factors. The paper treats ESGC as a new and important component of bank risk, amplified by banks’ exposure to media and the need for effective risk governance.\"},{\"question\":\"How does the research predict ESG controversies in banks?\",\"answer\":\"The study uses machine learning with a focus on feature selection. It selects relevant attributes from a large set of variables to build prediction models for ESGC scores.\"},{\"question\":\"What data and time period are used in the study?\",\"answer\":\"The research relies on a real panel dataset covering 140 banks. Measurements span the 2011–2020 period using 28 variables as features and an ESGC score as the prediction target.\"}]","Predicting ESG Controversies in Banks Using Machine Learning Techniques - Research Article | PDF",1785725158,50,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-esg-controversies-in-banks-using-machine-learning-techniques-research-article","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-esg-controversies-in-banks-using-machine-learning-techniques-research-article/119589/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is ESGC and why is it important for banks?","Question",{"text":75,"@type":76},"ESGC refers to controversies arising from the mistreatment of environmental, social, and governance (ESG) factors. The paper treats ESGC as a new and important component of bank risk, amplified by banks’ exposure to media and the need for effective risk governance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the research predict ESG controversies in banks?",{"text":80,"@type":76},"The study uses machine learning with a focus on feature selection. It selects relevant attributes from a large set of variables to build prediction models for ESGC scores.",{"name":82,"@type":73,"acceptedAnswer":83},"What data and time period are used in the study?",{"text":84,"@type":76},"The research relies on a real panel dataset covering 140 banks. Measurements span the 2011–2020 period using 28 variables as features and an ESGC score as the prediction target.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]