[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119003-en":3,"doc-seo-119003-105":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},119003,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Can we trust machine learning to predict the credit risk of small businesses?","With the rise of Fintech lending, small businesses increasingly rely on standardized, advanced machine-learning systems to assess creditworthiness using limited information. This paper evaluates whether machine learning can accurately predict credit risk ratings for small firms using a proprietary dataset drawn from invoice lending activities. Results indicate machine-learning methods outperform traditional models such as probit when lenders have restricted information, supporting a more reliable application of advanced credit scoring in Fintech credit decisions for small businesses.","Review of Quantitative Finance and Accounting [https://doi.org/10.1007/s1](https://doi.org/10.1007/s1)1156-024-01278-0  \nORIGINAL RESEARCH  \nCan we trust machine learning to predict the credit risk of small businesses?  \nAlessandro Bitetto1 · Paola Cerchiello1 · Stefano Filomeni2 · Alessandra Tanda1 · Barbara Tarantino1  \nAccepted: 28 March 2024 © The Author(s) 2024  \nAbstract  \nWith the emergence of Fintech lending, small firms can benefit from new channels of financing. In this setting, the creditworthiness and the decision to extend credit are often based on standardized and advanced machine-learning techniques that employ limited information. This paper investigates the ability of machine learning to correctly predict credit risk ratings for small firms. By employing a unique proprietary dataset on invoice lending activities, this paper shows that machine learning techniques overperform traditional techniques, such as probit, when the set of information available to lenders is limited. This paper contributes to the understanding of the reliability of advanced credit scoring techniques in the lending process to small businesses, making it a special interesting case for the Fintech environment.  \nKeywords Small businesses · Credit rating · Credit risk · Invoice lending · Machine learning · Fintech  \nJEL classification C52 · C53 · D82 · D83 · G21 · G22  \n1 Introduction  \nSmall businesses have always struggled to obtain funding through traditional channels because of their limited size and high information asymmetries (Sharpe 1990 ; Ivashina 2009) . This acts as an obstacle to their potential growth and development in the marketplace (Berger and Udell 2006) .  \nAmong the financing channels, bank lending has always received great attention by the extant literature (Agostino et al. 2012; Canales and Nanda 2012; Grunert and Norden 2012; Beck 2013) . Indeed, especially in the past, banks were able to overcome information asymmetries through “relationship lending” that allowed them to incorporate borrower-specific soft information in the lending process, thus enabling informationally opaque small businesses  \n* Stefano Filomeni  \n[stefano.filomeni@essex.ac.uk](stefano.filomeni@essex.ac.uk)  \n1 University of Pavia, Department of Economics and Management, Pavia, Italy  \n2 University of Essex, Essex Business School, Finance Group, Colchester, UK  \n1 3  \nto be financed (Filomeni et al. 2021) . After the regulatory evolution of the Basel Accords, the employment of soft information has become more and more difficult due to limitations to hardening soft information in banks’ internal ratings, thus causing a shift in the preferences of banks towards alternative ways to assess corporate creditworthiness that left small businesses lending demand somehow unmet (Berger 2006; Filomeni et al., 2021; 2020) .  \nIn this context, policymakers and small businesses have welcomed the birth and diffusion of Fintech (financial technology) and the application of advanced methodologies in the field of banking and finance. The Fintech revolution has brought new ways to interact with small businesses that can finally have a timely response to their financing needs by new and old lenders (Tanda and Schena 2019; Gong and Ribiere 2021) .  \nFintech companies developed especially in the segment of invoice lending (sometimes referred to as “trade credit”) (Dorfleitner et al. 2017) . Invoice lending enables firms to obtain new resources via the presentation of invoice receivables or other credit instruments to be discounted (Soufani 2002) . Firms that issue invoices (creditors to their customers) can therefore apply for the “anticipation” of the discounted amount exhibited on the invoice (or another credit instrument), essentially employing the unpaid customer invoices as collateral to obtain immediate cash from a lender. It is a form of short-term borrowing that provides businesses with quick access to working capital. It is a widespread form of financing that ena","cbCaiuM1kgJCNGPE","https://ap.wps.com/l/cbCaiuM1kgJCNGPE","pdf",1914169,1,30,"English","en",105,"# Abstract\n# Introduction\n## Small businesses and information asymmetries\n## Bank lending and the role of soft information\n## Fintech and invoice lending\n## Creditworthiness assessment and business models","[{\"question\":\"What problem does the paper address about small businesses and lending?\",\"answer\":\"Small businesses often struggle to obtain funding due to limited size and high information asymmetries, which make standard lending assessments less effective.\"},{\"question\":\"How does the paper test machine learning for credit risk prediction?\",\"answer\":\"It uses a proprietary dataset from invoice lending activities to predict credit risk ratings and compares machine learning performance against traditional approaches such as probit.\"},{\"question\":\"What is the main finding regarding machine learning versus traditional techniques?\",\"answer\":\"Machine learning methods overperform traditional techniques when the lender’s available information is limited, improving credit risk rating prediction.\"}]","Can we trust machine learning to predict the credit risk of small businesses? 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