Identification and Analysis of Credit and Behavioral Indicators: A Model for Ranking Retail Banking Loan Customers

Document Type : Research Paper

Authors

1 Ph.D. Candidate, Department of Economic Sciences, Qeshm Branch, Islamic Azad University, Qeshm, Iran.

2 Assistant Prof., Department of Financial Management, Qeshm Branch, Islamic Azad University, Qeshm, Iran.

3 Associate Prof., Department of Accounting and Financial Management, Faculty of Humanities, Bandar Abbas Branch, Islamic Azad University, Bandar Abbas, Iran.

4 Associate Prof., Department of Business Management & Financial Management, Islamshahr Branch, Islamic Azad University, Tehran, Iran.

10.22059/frj.2025.388263.1007692

Abstract

Objective
To score customers effectively, it is essential to establish a fair and appropriate scoring system. This system should classify customers into different categories based on credit and behavioral criteria and assign them scores aligned with their performance. Moreover, developing methods to evaluate and monitor customers over time is necessary. This study aims to identify and analyze credit and behavioral indicators to propose a model for ranking customers with unsecured small loans. Customer credit evaluation is a complex process that involves reviewing documents, analyzing financial status, and assessing the customer's payment history. A critical aspect of this evaluation is determining customers' ability, willingness, and capacity to repay loans. This research seeks to present a credit evaluation and ranking model for unsecured small loans and to estimate the probability of default within the bank’s digital banking system.
 
Methods
The statistical population for this research includes all retail customers of the digital banking services of Khavar-e-Miyaneh Bank. A census method was used to study the customers during the fiscal years 1400–1401. The model developed by Ahmadi Koosha and colleagues (1403) was used as the basis for ranking customers with unsecured small loans. Indicators such as age, loan amount, eligible loan request amount, total loans received, score, anti-money laundering approval, gender, occupation, city, education level, loan status, and job type were identified as the initial input variables. Data analysis was conducted using fuzzy regression and was inspired by Python libraries such as SciKit-Fuzzy and NumPy, resulting in a fuzzy regression formula. To finalize the evaluation of the credit scoring and ranking model, an example involving a customer was presented.
 
Results
The results indicate that variables such as the requested loan amount, total loans received, and occupation do not influence the repayment of installments 60 days past due. The ranking of significant variables affecting repayment within 60 days after the due date is as follows: loan settlement status, anti-money laundering status, gender, education level, age, score, job type, and city. Additionally, the model’s accuracy and predictive power were tested using a hypothetical customer, yielding a score of 0.8738. This score demonstrates that the customer is in a favorable position for repayment within 60 days past due and highlights the model's efficiency in credit risk assessment and management, which are crucial for financial institutions to ensure sustainable lending practices.
 
Conclusion
The results indicate that variables such as the requested loan amount, total loans received, and occupation do not affect the repayment of installments 60 days past due. The ranking of significant variables influencing repayment within 60 days after the due date is as follows: loan settlement status, anti-money laundering status, gender, education level, age, score, job type, and city. Furthermore, the model’s accuracy and predictive power were tested using a hypothetical customer, yielding a score of 0.8738. This score indicates that the customer is in a favorable position for repayment within 60 days past due and demonstrates the model's effectiveness in credit risk assessment and management, which are crucial for financial institutions to ensure sustainable lending practices.

Keywords

Main Subjects

References

 
Ahmadi Kousha, A., Ahmadi, F., Ranjbar, M. H. & Kordlouie, H. (2024). Validation Indicator Identification and Customer Ranking in Microloans: A Study at Middle East Bank in Iran. Financial Research Journal, 26(2), 415-438. doi: 10.22059/frj.2024.370376.1007551 (in Persian)
Ahmadi Sartakhti, F., Hojabr Kiani, K., Hoseini, S. S. & Memarnejad, A. (2023). Designing a Model for Credit Risk Assessment of Customers for Guarantees Issued by the Export Guarantee Fund of Iran via Artificial Neural Network Model. Financial Research Journal, 25(4), 641-660. doi: 10.22059/frj.2023.361963.1007488 (in Persian)
Akhbari, H., Mohammadzadeh Salteh, H., Baradaran Hassanzadeh, R. & Zeynali, M. (2024). Optimizing Risk-based Stock Return Prediction in Tehran Stock Exchange industries: A Data Envelopment Analysis. Financial Research Journal, 26(2), 347-370. doi: 10.22059/frj.2023.339775.1007309 (in Persian)
Almaiah, M. A., Alfaisal, R., Salloum, S. A., Al-Otaibi, S., Al Sawafi, O. S., Al-Maroof, R. S., ... & Awad, A. B. (2022). Determinants influencing the continuous intention to use digital technologies in Higher Education. Electronics, 11(18), 2827.
Alrawad, M., Lutfi, A., Almaiah, M. A., Alsyouf, A., Al-Khasawneh, A. L., Arafa, H. M., ... & Tork, M. (2023). Managers’ Perception and Attitude toward Financial Risks Associated with SMEs: Analytic Hierarchy Process Approach. Journal of Risk and Financial Management, 16(2), 86.
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589-609.
An, X., Cordell, L. & Tang, S. (2020). Extended Loan Terms and Auto Loan Default Risk. Research Department, Federal Reserve Bank of Philadelphia.
Balkrishna, A., Ghosh, S. & Arya, V. (2024). A Case Study on the Role of Digital Intervention in the Success of Micro-Finance through SIDBI-PRAYAAS Scheme. International Journal of Financial Management, 14(2).
Bello, O. A. (2023). Machine learning algorithms for credit risk assessment: an economic and financial analysis. International Journal of Management, 10(1), 109-133.‏
Beltrame, F., Grassetti, L., Bertinetti, G. S. & Sclip, A. (2023). Relationship lending, access to credit and entrepreneurial orientation as cornerstones of venture financing. Journal of Small Business and Enterprise Development, 30(1), 4-29.
Bhatt, T. K., Ahmed, N., Iqbal, M. B. & Ullah, M. (2023). Examining the Determinants of Credit Risk Management and Their Relationship with the Performance of Commercial Banks in Nepal. Journal of risk and financial management, 16(4), 235.
Bhattacharya, A., Biswas, S. K. & Mandal, A. (2023). Credit risk evaluation: a comprehensive study. Multimedia Tools and Applications, 82(12), 18217-18267.‏
Cai, J., Meki, M., Quinn, S., Field, E., Kinnan, C., Morduch, J., ... & Said, F. (2023). Microfinance. VoxDevLit, 3(2), 26.‏
Chen, N., Ribeiro, B. & Chen, A. (2016). Financial credit risk assessment: a recent review. Artificial Intelligence Review, 45, 1-23.‏
Cosma, S., Rimo, G. & Torluccio, G. (2023). Knowledge mapping of model risk in banking. International Review of Financial Analysis, 102800.‏
Creswell, J.W. (2003) Qualitative, Quantitative, and Mixed Methods Approaches, (2nd Edition). Sage publications.
Cull, R. & Hartarska, V. (2023). Overview of microfinance, financial inclusion, and development. In Handbook of microfinance, financial inclusion and development (pp. 2-19). Edward Elgar Publishing.‏
Dawodu, S. O., Omotosho, A., Akindote, O. J., Adegbite, A. O. & Ewuga, S. K. (2023). Cybersecurity risk assessment in banking: methodologies and best practices. Computer Science & IT Research Journal, 4(3), 220-243.‏
Eisazadeh, S. & Aryani, B. (2010). Ranking Legal Customers of Banks Based on Credit Risk Using Data Envelopment Analysis: A Case Study of Branches of Bank Keshavarzi. Economic Research and Policies, (55), 59-86. (in Persian)
Eshraghi Samani, R., Sheykh Mohammadi, F. & Poursaeed A.R. (2015). Effective Factors Contributing to the Non-Repayment of Keshavarzi bank Facilities by Farmers Case: Ilam County. Spatial Economics and Rural Development, 4(12), 77-91. (in Persian)
Fallah Tafti, M. (2021). Explaining Credit Risk in the Banking System. Eighth International Conference on Accounting, Management and Innovation in Business, Tehran.
(in Persian)
Fati, S. M. (2024). A Loan Default Prediction Model Using Machine Learning and Feature Engineering. ICIC Express Lett, 18(1), 27-37.‏
Ge, Y., Song, H., & Li, B. (2021, April). Bank Loan Strategy Based on Evaluation and Decision Model. In Journal of Physics: Conference Series, 1865(4), 042018. IOP Publishing.
Hota, L., Jain, P. K., & Kumar, A. (2025). A Comparative Performance Assessment for Prediction of Loan Approval in Financial Sector. Procedia Computer Science, 258, 298-307.
Joseph, N., Guérin, I., Guermond, V., Brickell, K., Natarajan, N. & Michiels, S. (2024). Microfinance, debt distress and data capture: Evidence from pandemic times in rural South India Research findings report (Doctoral dissertation, Royal Holloway University of London; French Institute of Pondicherry; King's College London; Princeton University).‏
Khojesteh, Gh., Dayi Karimzadeh, S. & Sharifi Renani, H. (2020). Credit Ranking of Retail Customers of Banks with a Combined Approach of Logistic Regression-Symbolic. Quarterly Journal of Human Resources Management in Law Enforcement, 7(3), 117-148. (in Persian)
Koohi, H. & Gholami, R. (2012). Credit Ranking of Corporate Customers in the Industrial Sector Using Data Envelopment Analysis (DEA) Model. Quantitative Management Studies, 3(3), 115-138. (in Persian)
Kumar, C. N., Keerthana, D., Kavitha, M. & Kalyani, M. (2022, June). Customer Loan Eligibility Prediction using Machine Learning Algorithms in Banking Sector. In 2022 7th International Conference on Communication and Electronics Systems (ICCES) (pp. 1007-1012). IEEE.
Li, Z., Li, A., Bellotti, A. & Yao, X. (2023). The profitability of online loans: A competing risks analysis on default and prepayment. European Journal of Operational Research, 306(2), 968-985.‏
Liu, Z., Zhang, Z., Yang, H., Wang, G. & Xu, Z. (2023). An innovative model fusion algorithm to improve the recall rate of peer-to-peer lending default customers. Intelligent Systems with Applications, 20, 200272.‏
Meng, B., Sun, J. & Shi, B. (2024). A novel URP-CNN model for bond credit risk evaluation of Chinese listed companies. Expert Systems with Applications, 255, 124861.‏
Murphy, C. (2024). ‘Trust No One’: The Logics of Microfinance, Depending on Whom You Ask. In Who Gives to Whom? Reframing Africa in the Humanitarian Imaginary (pp. 155-174). Cham: Springer Nature Switzerland.‏
Naili, M. & Lahrichi, Y. (2022). The determinants of banks' credit risk: Review of the literature and future research agenda. International Journal of Finance & Economics, 27(1), 334-360.
Nguyen, Q. K. (2022). Audit committee structure, institutional quality, and bank stability: Evidence from ASEAN countries. Finance Research Letters, 46, 102369.
Pimcharee, K. & Surinta, O. (2022). Data Mining Approaches in Personal Loan Approval. Engineering Access, 8(1), 15-21.
Pomazanov, M. (2022). Validation of the effectiveness of the bank retail portfolio risk management procedure. Procedia Computer Science, 199, 798-805.
Pur, S., Huesig, S. & Schmidhammer, C. (2022). Application and validation of a disruptive potential methodology for digital two-sided platforms-the case of marketplace lending in Germany. International Journal of Technology Management, 88(2-4), 205-246.
Rahmani, A., Parsaei, M. & Mohammadi Khanghah, G. (2023). Credit Rating and Cost of Capital. Financial Research Journal, 25(1), 110-126. doi: 10.22059/frj.2022.342131.1007325 (in Persian)
Saha, S. & Waheed, S. (2017). Credit risk of bank customers can be predicted from customer's attribute using neural network. International Journal of Computer Applications, 161(3), 39-43.
Samsir, S., Suparno, S. & Giatman, M. (2020, April). Predicting the loan risk towards new customer applying data mining using nearest neighbor algorithm. In IOP Conference Series: Materials Science and Engineering (Vol. 830, No. 3, p. 032004). IOP Publishing.
Sarker, D. (2022). Experiences of people with physical disabilities when accessing microfinance services in Bangladesh: A qualitative study. Alter. European Journal of Disability Research, (3), 41-55.
Scott, A. O., Amajuoyi, P. & Adeusi, K. B. (2024). Effective credit risk mitigation strategies: Solutions for reducing exposure in financial institutions. Magna Scientia Advanced Research and Reviews, 11(1), 198-211.‏
Serkanian, J., Raei, R., Shirkavand, S. & Abbasian, E. (2023). Evaluating the Effect of Bank Characteristics on Bank Lending Channel: A Factor-augmented Vector Autoregressive (FAVAR) Approach. Financial Research Journal, 25(1), 1-25. doi: 10.22059/frj.2021.327426.1007220 (in Persian)
Simão, S. B. S. (2023). Machine Learning applied to credit risk assessment: Prediction of loan defaults (Master's thesis, Universidade NOVA de Lisboa (Portugal)).‏
Song, Y., Wang, Y., Ye, X., Zaretzki, R. & Liu, C. (2023). Loan default prediction using a credit rating-specific and multi-objective ensemble learning scheme. Information Sciences, 629, 599-617.
Sundar, R. (2021). Impact of Pre Loan assessment customer credit worthiness on loan defaults at later stages in Rural Segment–a study at Vehicle Financing NBFC. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 232-240.‏
Torabian, A., Nahidi Amirkhiz, M. R., Javani, S. & Hassanzadeh, R. (2022). Credit Scoring and Ranking of Retail Customers: A Case Study of Bank Saderat Iran. Investment Knowledge, 11(41), 145-162. (in Persian)
Wang, L. (2022). Imbalanced credit risk prediction based on SMOTE and multi-kernel FCM improved by particle swarm optimization. Applied Soft Computing, 114, 108153.
Wang, W., Zhang, Y., Li, Y., Hu, Q., Liu, C. & Liu, C. (2022). Vulnerability analysis method based on risk assessment for gas transmission capabilities of natural gas pipeline networks. Reliability Engineering & System Safety, 218, 108150.
Xu, Y. (2020). Research on Personal Medical Loan under Bank-Medical Cooperation Mode. 2020 International Conference on the Frontiers of Innovative Economics and Management (FIEM 2020).
Yao, G., Hu, X. & Wang, G. (2022). A novel ensemble feature selection method by integrating multiple ranking information combined with an SVM ensemble model for enterprise credit risk prediction in the supply chain. Expert Systems with Applications, 200, 117002.
Zeng, H. (2019). Analysis and Research on the Profit Contribution Model of Commercial Bank Customers Based on Credit Rating Model, 2019 7th International Education, Economics, Social Science, Arts, Sports and Management Engineering Conference (IEESASM 2019).
Zhang, L., Wang, J. & Liu, Z. (2023). What should lenders be more concerned about? Developing a profit-driven loan default prediction model. Expert Systems with Applications, 213, 118938.‏
Zolfaghari, R., Tashakori, N. & Eram, A. (2022). Designing Collaterals Assessment Model to Finance Technological Projects and SMEs by Adaptive Neural Fuzzy Inference System (ANFIS). Financial Research Journal, 24(3), 453-479. doi: 10.22059/frj.2022.313263.1007094 (in Persian)
Zuama, R. A., Ichsan, N., Pohan, A. B., Azis, M. S. & Lase, M. (2024). An implementation of machine learning on loan default prediction based on customer behavior. Jurnal Info Sains: Informatika dan Sains, 14(01), 157-164.‏
Financial Research Journal
Volume 27, Issue 4
2025
Pages 960-986

Files

Share

How to cite

Statistics