Classification Algorithms in Financial Application: Credit Risk Analysis on Legal Entities

Machine learning predictivity applied to consumer creditworthiness

“…Within the context of credit risk research using machine learning techniques, there are several studies that seek to analyze the adequacy of the models in specific databases [1,25,35]. However, the literature has not yet identified techniques that consistently lead to higher credit prediction accuracy [10].…”

“…Outside this repository, Feng et al [12] also examined Chinese credit data, as well as, Li et al [19] and Moula et al [22]. In Latin America, Assef et al and Vieira et al [1,31] analyzed a set of a Brazilian bank, Morales et al [21] explored Peruvian microfinance data. Besides that, numerous cases can be cited, such as [7] (France), [18] (Nigeria), [23] (Greece), [8,11] (UK), and [20] (61 countries).…”

“…Luo, Pl awiak et al, and Twala [ 20 , 25 , 30 ] established that credit risk assessment is an important issue in financial risk management, because banks should make important decisions about whether or not make a loan to a counterparty. In this context, Assef et al [ 1 ] suggest that one of the main problems in finance involves the prediction of bankruptcy or default.…”

“…We investigate an unusual credit portfolio, due to its high default rate. It is important to highlight that other papers have studied Brazilian datasets using machine learning such as Assef et al [ 1 ] that explored 6,000 firms that applied for loans and Vieira et al [ 31 ] that investigated mortgages for low-income borrowers. However, despite some papers analyze emerging countries, most published papers focus on developed countries, which data is usually more available to researchers.…”

“…In this context, the article aims to contribute to the literature, still under development, on the adequacy of machine learning techniques for the phenomena related to the classification of observations, more particularly for credit risk analysis, as studied by Assef et al, Crone and Finlay, Pl awiak et al, Shi et al, Xiao et al, Yeh and Lien [ 1 , 8 , 25 , 27 , 33 , 34 ], among others.…”