Mathematical Modeling and Analysis of Credit Scoring Using the LIME Explainer: A Comprehensive Approach

Abdussalam Aljadani, Bshair Alharthi, Mohammed A. Farsi, Hossam Magdy Balaha, Mahmoud Badawy and Mostafa A. Elhosseini ()
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Abdussalam Aljadani: Department of Management, College of Business Administration in Yanbu, Taibah University, Al-Madinah Al-Munawarah 41411, Saudi Arabia
Bshair Alharthi: Department of Marketing, College of Business, University of Jeddah, Jeddah 22425, Saudi Arabia
Mohammed A. Farsi: College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia
Hossam Magdy Balaha: Bioengineering Department, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40208, USA
Mahmoud Badawy: Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Mostafa A. Elhosseini: College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia

Mathematics, 2023, vol. 11, issue 19, 1-28

Abstract: Credit scoring models serve as pivotal instruments for lenders and financial institutions, facilitating the assessment of creditworthiness. Traditional models, while instrumental, grapple with challenges related to efficiency and subjectivity. The advent of machine learning heralds a transformative era, offering data-driven solutions that transcend these limitations. This research delves into a comprehensive analysis of various machine learning algorithms, emphasizing their mathematical underpinnings and their applicability in credit score classification. A comprehensive evaluation is conducted on a range of algorithms, including logistic regression, decision trees, support vector machines, and neural networks, using publicly available credit datasets. Within the research, a unified mathematical framework is introduced, which encompasses preprocessing techniques and critical algorithms such as Particle Swarm Optimization (PSO), the Light Gradient Boosting Model, and Extreme Gradient Boosting (XGB), among others. The focal point of the investigation is the LIME (Local Interpretable Model-agnostic Explanations) explainer. This study offers a comprehensive mathematical model using the LIME explainer, shedding light on its pivotal role in elucidating the intricacies of complex machine learning models. This study’s empirical findings offer compelling evidence of the efficacy of these methodologies in credit scoring, with notable accuracies of 88.84%, 78.30%, and 77.80% for the Australian, German, and South German datasets, respectively. In summation, this research not only amplifies the significance of machine learning in credit scoring but also accentuates the importance of mathematical modeling and the LIME explainer, providing a roadmap for practitioners to navigate the evolving landscape of credit assessment.

Keywords: credit scoring; empirical analysis; feature selection; LIME explainer; machine learning (ML); particle swarm optimization (PSO) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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