Comprehensive Classification of Credit Scoring Models

Subject Areas : business management

Pardis Fooladi ¹ , Mohsen Amini Khozani ² , Zohreh Hajiha ³ , Shadi Shahverdiani ⁴

1 - PhD student, Department of Financial Engineering, Faculty of Humanities, , Shahr Ghods Branch, Islamic Azad University, Tehran, Iran
2 - Assistant Professor, Department of Financial Engineering, Shahr-e-Quds Branch, Islamic Azad University, Tehran, Iran
3 - Associate Professor, Accounting Department, East Tehran Branch, Islamic Azad University, Tehran, Iran
4 - Assistant Professor, Department of Financial Engineering, Shahr-e-Quds Branch, Islamic Azad University, Tehran, Iran

Received: 2024-07-24 Accepted : 2024-11-30 Published : 2025-02-24

Keywords: Credit scoring, statistical analysis, regression, machine learning, neural networks.,

Abstract :

Credit scoring models have evolved significantly over time, incorporating various techniques to assess the creditworthiness of individuals and businesses. Credit scoring is an important process for credit providers because it determines the probability of default or non-default of applicants and plays an important role in determining people's ability to receive facilities and credit. Traditional credit scoring models, such as those that incorporate the credit history and repayment behavior of borrowers, have been of interest in credit risk management for decades. These models help decision-making processes by evaluating credit, influencing loan approvals, and shaping preventive measures in case of default. Using emerging machine learning frameworks and approaches and alternative data sources, credit scoring methods will become more successful over time in optimizing risk management, improving loan approval processes, reducing investment risk for lenders, and effectively addressing the financial needs of borrowers. In this research, a comprehensive classification of credit scoring models has been presented using bibliometric citation analysis and includes more than 100 cited articles.

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