Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models

Faraz Ahmed, Kehkashan Nizam, Zubair Sajid, Sunain Qamar and Ahsan
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Faraz Ahmed: PhD Scholar, Department of Business Administration, IQRA University, Karachi, Pakistan
Zubair Sajid: Lecturer, Department of Computer Science, IQRA University, Karachi, Pakistan
Sunain Qamar: MBA, Department of Management Sciences, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology University, Karachi, Pakistan
Ahsan: Lecturer, Department of Commerce, Benazir Bhutto Shaheed University, Lyari, Karachi, Pakistan

Bulletin of Business and Economics (BBE), 2024, vol. 13, issue 3, 30-35

Abstract: This research assesses machine learning models' validity, clarity, and equity, compared to classical models and especially logistic regression in credit risk evaluation. In the traditional model of data management, efficiency and the accuracy of information are challenges; an issue of machine learning models is model selection and multicollinearity. The study intends to help financial institutions establish the best strategy for their needs. Furthermore, it delves into the effect of heterogeneous data sources on the credit risk model using machine learning. The research analyses the implications of using machine learning in assessing credit risk. Interestingly, focusing on peer-to-peer lending platforms, the research aims to deal with the need for more attention to combining machine learning and traditional models in the literature. The deductive method is the application of inferential analyses, the Traditional model is logistic regression, and the Machine Learning model is a neural network (CNN model) based on secondary data from the Kaggle peer-to-peer lending dataset. With likely findings expected to comprise prediction of the probability of default and better availability of loans, risk analysis leads to formulated lending decisions managing a financial portfolio.

Keywords: Credit Risk; Credit Scoring; Machine Learning Model; Traditional Model; Default (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:rfh:bbejor:v:13:y:2024:i:3:p:30-35

DOI: 10.61506/01.00425

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