Financial institutions use a variety of methodologies to define their commercial and strategic policies, and a significant role is played by credit risk assessment. In recent years, different credit risk assessment services arose, providing Social Lending platforms to connect lenders and borrowers in a direct way without assisting of financial institutions. Despite the pros of these platforms in supporting fundraising process, there are different stems from multiple factors including lack of experience of lenders, missing or uncertain information about the borrower's credit history. In order to handle these problems, credit risk assessments of financial transactions are usually modeled as a binary problem based on debt repayment, going to apply Machine Learning (ML) techniques. The paper represents an extended abstract of a recent work, where some of the authors performed a benchmarking among the most used credit risk assessment ML models in the field of predicting whether a loan will be repaid in a P2P platform. The experimental analysis is based on a real dataset of Social Lending (Lending Club), going to evaluate several evaluation metrics including AUC, sensitivity, specificity and explainability of the models.
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