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

Author

Listed:
  • Faraz Ahmed

    (PhD Scholar, Department of Business Administration, IQRA University, Karachi, Pakistan)

  • Kehkashan Nizam

    (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)

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.

Suggested Citation

  • Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
    • Handle: RePEc:rfh:bbejor:v:13:y:2024:i:3:p:30-35
    DOI: https://doi.org/10.61506/01.00425
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    References listed on IDEAS

    as
    1. Ahmed Almustfa Hussin Adam Khatir & Marco Bee, 2022. "Machine Learning Models and Data-Balancing Techniques for Credit Scoring: What Is the Best Combination?," Risks, MDPI, vol. 10(9), pages 1-22, August.
    2. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
    3. Andrés Alonso & José Manuel Carbó, 2020. "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers 2032, Banco de España.
    4. K. S. Naik, 2021. "Predicting Credit Risk for Unsecured Lending: A Machine Learning Approach," Papers 2110.02206, arXiv.org.
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