Default Prediction for Loan Lenders Using Machine Learning Algorithms


  • Awuza Abdulrashid Egwa Federal University Gashua, Yobe State
  • Habeeb Bello Department of Electronics and Telecommunications Engineering, Ahmadu Bello University Zaria, Nigeria
  • Ahamad Ajiya Ahmad Department of Computer Science, Federal University Gashua, Yobe State, Nigeria
  • Suleiman Musa Bizi Department of Computer Science, Federal University Gashua, Yobe State, Nigeria


Prediction; loan default; early repayment; Machine learning


Credit loans are considered most essential aspect of most financial institutions. All loan mortgagees or lenders are demanding to identify out effective commercial and business approaches to encourage customers to apply their credit loans. There are numerous business patrons who act negatively after their requests got approval. To avert this condition, lenders have to discover some techniques to forecast customer’s behaviors. This resulted to the usage of machine learning algorithms by the financial lending institutions for accessing loan applicants. Despite advancements in automating decision-based loan systems, most existing models do not consider the “early loan repayment” attribute as a factor in resolving this prediction error. In reality, the amendment for preliminary loan reimbursement in model building is obligatory, since a larger numbers of timely loan reimbursement observed during the loan period, reduces default rate. For effective model’s comparison based on accuracy and minimum errors of prediction, six supervised machine learning algorithms i.e. Random Forest, Artificial Neural Network, Classification and Regression Tree, Support Vector Machine, Logistic Regression, and Naïve Bayes were adopted to develop a default prediction models which include the early loan repayment attribute. The models were trained and tested on a loan dataset consisting of attributes with, and without early loan repayment attribute and were evaluated using five performance metrics. The results of the performance evaluation show that models that account for early loan repayment have higher accuracy, recall, precision, Root Mean Square Error and Receiver Operative Characteristics curve values than models trained without the early loan repayment attribute. The Random forest model proofed to be the best predictive model having 93% accuracy, 11% RMSE, 90% precision, 89% recall and 81% ROC value over others models.



Alaka, H. A., Oyedele, L. O., Owolabi, H. A., Kumar, V., Ajayi, S. O., Akinade, O. O., and Bilal, M. (2018). Systematic review of bankruptcy prediction models: Towards a framework for tool selection. Expert Systems with Applications, 94, 164-184.

Awuza, A. E., Habeebah, K. A., Ahmad, A. A., Abubakar, M. B. and Muhammad, A. M. (2022). Prediction Model for Loan Default Using Machine Learning. The International Journal Of Science & Technoledge. DOI No.: 10.24940/theijst/2022/v10/i2/ST2202-009

Bao, W., Yue, K., Yongtao, Z., Dapeng, L. and Lianju, N.(2019) Integration of Unsupervised and Supervised Machine Learning Algorithms for Credit Risk Assessment, Expert Systems With Applications`

Begum, C. and Deniz, U. (2019) Comparison of Data Mining Classification Algorithms: Determining the Default Risk. Research Article, Hindawi Scientific Programming Volume 2019, Article ID 8706505, 8 pages

Carta, V., Ferreira, A., Recupero, D. R., Saia, M. and Saia, R. (2020) A combined entropy-based approach for a proactive credit scoring. Engineering Applications of Artificial Intelligence. 87. 103292.

Chen, N., Ri, B., kijbeiro and Chen, A. (2016) Financial credit risk Assessment: a recent review. Artificial Intelligence Review, 45th ed. 1, pp. 1–23.

Chow, J. C. (2018) Analysis of Financial Credit Risk Using Machine Learning. 2018 arXiv preprint arXiv:1802.05326.

Cortes, C. and Vapnik, V. (1995) Support-vector networks. Machine Learning,. 20, pp. 273–297.

Deng, X. Liu Q. and Deng,Y. (2016) An improved method to construct basic probability assignment based on the confusion matrix for classification problem . Information Sciences, 340, pp. 250-261.

Dirick, L. Claeskens, G. and Baesens, B. (2017). Time to default in credit scoring using survival analysis: a benchmark study, Journal of the Operational Research Society. 68, pp. 652–665

Djeundje, V. B. and Crook, J. (2018) Incorporating heterogeneity and macroeconomic variables into multi-state delinquency models for credit cards. European Journal of Operational Research, 2nd ed. 271, pp. 697-709.

Fu, Y. J. (2017). Combination of Random Forests and Neural Networks in Social Lending. Journal of Financial Risk Management, 6, 418-426.

Gaigaliene, A. and cesnys, D. (2018). Determinants of default in Lithuanian peer-to-peer platforms. Organizacijų vadyba: sisteminiai tyrimai= Management of organizations: systematic research. Kaunas: Vytauto Didžiojo universitetas.

Gulsoy, N. and Kulluk, S. (2019). A data mining application in credit scoring processes of small and medium enterprises commercial corporate customers. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 9 (3), e1299.

Huo, Y.J., Chen, H.Z. and Chen, J.C. (2017) Research on Personal Credit Assessment Based on Neural Network-Logistic Regression Combination Model. Open Journal of Business and Management, 5, 244-252.

Jiang C., Wang Z. and Zhoo, H. (2019). A Prediction-driven Mixture cure model and its Application in Credit Scoring. European Journal of operational Research. 277, pp20-31

Lessmann, S., Baesens, B., Seow,H. V. and Thomas, L. C. (2015) Benchmarking state-of-the-art classification algorithms for credit scoring: an update of research. European Journal of Operational Research. 1( 247) pp. 1-32.

Lee, T. S., Chiu, C. C., Chou, Y. C., & Lu, C. J. (2006). Mining the customer credit using classification and regression tree and multivariate adaptive regression splines. Computational Statistics & Data Analysis, 50(4), 1113-1130.

Liu, F., Hua, Z. and Lim, A. (2015). Identifying future defaulters: A hierarchical Bayesian method. European Journal of Operational Research. 241, pp. 202–211.

Luo, S., Kong, X. and Nie, T. (2016). Spline based survival model for credit risk modeling. European Journal of Operational Research. 3(253) pp. 869–879

Malekipirbazari, M. and Aksakalli, V. (2015) Risk assessment in social lending via random forests. Expert Systems with Applications. 42( 10), pp. 4621-4631

Nok, W. M. (2017). Bankruptcy Prediction of Industrial Industry in the UK. Sriwijaya international journal of dynamic Economics and Business. 1(1), pp. 1-26.

Padimi V., Venkata S. T. and Devarani D. N. (2022). Applying Machine Learning Techniques To Maximize The Performance of Loan Default Prediction. Journal of Neutrosophic and Fuzzy Systems (JNFS). Vol. 2, No. 2, PP. 44-56.

Quinlan, J.R. (1993) C4.5: Programs for machine learning. Morgan Kaufmann Publishers, Massachusetts.

Rehman, N. (2017). Data mining techniques, method, algorithms and tools. International Journal of computer science and mobile computing . 6, pp. 227-231.

Rosenberg, E. and Gleit, A. (1994) Quantitative methods in credit management: a survey. Operations Research. 42(4) pp. 589-613.

Thomas, L., Crook, J. and Edelman, D. (2017). Credit scoring and its applications. Society for industrial and Applied Mathematics.

Wu Q. (2022). "Real-time Predictive Analysis of Loan Risk with Intelligent Monitoring and Machine Learning Technique,"2022 IEEE 4th International Conference on Power, Intelligent Computing and Systems (ICPICS), 2022, pp. 852-856, doi: 10.1109/ICPICS55264.2022.9873618.

Ying, L. (2018). Research on bank credit default prediction based on data mining algorithm. The International Journal of Social Sciences and Humanities Invention . 5(6) pp. 4820-4823.