A benchmark of machine learning approaches for credit score prediction. (1st March 2021)

• Record Type: Journal Article
• Authors: Moscato, Vincenzo; Picariello, Antonio; Sperlí, Giancarlo
• Is Part Of: Expert systems with applications. Volume 165(2021)
• Publication Date: 2021-03-01
• Subjects: Credit score prediction -- Benchmark -- Supervised learning -- Machine learning -- Explainable artificial intelligence; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113986
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: Credit risk assessment plays a key role for correctly supporting financial institutes in defining their bank policies and commercial strategies. Over the last decade, the emerging of social lending platforms has disrupted traditional services for credit risk assessment. Through these platforms, lenders and borrowers can easily interact among them without any involvement of financial institutes. In particular, they support borrowers in the fundraising process, enabling the participation of any number and size of lenders. However, the lack of lenders' experience and missing or uncertain information about borrower's credit history can increase risks in social lending platforms, requiring an accurate credit risk scoring. To overcome such issues, the credit risk assessment problem of financial operations is usually modeled as a binary problem on the basis of debt's repayment and proper machine learning techniques can be consequently exploited. In this paper, we propose a benchmarking study of some of the most used credit risk scoring models to predict if a loan will be repaid in a P2P platform. We deal with a class imbalance problem and leverage several classifiers among the most used in the literature, which are based on different sampling techniques. A real social lending platform ( Lending Club ) data-set, composed by 877, 956 samples, has been used to perform the experimental analysis considering different evaluation metrics (i.e. AUC, Sensitivity, Specificity), also comparing the obtained outcomes with respect to the state-of-the-art approaches. Finally, the three best approaches have also been evaluated in terms of their explainability by means of different eXplainable Artificial Intelligence (XAI) tools. Highlights: A credit risk scoring models benchmarking in P2P platform has been proposed. The class imbalance problem has been addressed using different sampling strategies. The evaluation has been made on a real P2P data-set composed by 877, 956 samples. The outcomes have been compared with state-of-the art approaches on different metrics. The three best models have been also evaluated in terms of their explainability.