2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 10 , Issue 2 , PP: 32-38, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Khyati Chaudhary 1 * , Gopal Chaudhary 2
Doi: https://doi.org/10.54216/AJBOR.100204
Credit risk assessment is a critical task for financial institutions to determine the creditworthiness of their potential customers. Business intelligence (BI) and machine learning (ML) techniques have gained popularity in recent years as effective tools for credit risk assessment. In this paper, we propose a decision support system (DSS) for credit risk assessment that integrates BI and ML techniques. The proposed DSS employs BI tools to extract and transform data from various sources, and ML techniques to analyze the data and generate predictive models for credit risk assessment. We evaluate the proposed DSS using a real-world dataset of a financial institution. The results show that the proposed DSS achieves a high level of accuracy in credit risk assessment. The results showed that the system was able to accurately predict credit risk, with an accuracy of 88%. The system also outperformed traditional credit scoring models, which highlights the potential of our system for credit risk assessment. The system provides decision-makers with actionable insights to make informed decisions, thereby reducing the risk of default and increasing the profitability of the financial institution.
Decision Support System , Credit Risk Assessment , Business Intelligence , Machine Learning
[1] Shazly, K., & Khodadadi, N. (2022). Credit card clients classification using hybrid guided wheel with particle swarm optimized for voting ensemble. Journal of Artificial Intelligence and Metaheuristics, 2(1), 46-54
[2] Shivanna, A., & Agrawal, D. P. (2020, November). Prediction of defaulters using machine learnin g on Azure ML. In 2020 11th IEEE annual information technology, electronics and mobile communication conference (IEMCON) (pp. 0320-0325). IEEE.
[3] Medeiros Assef, F., & Arns Steiner, M. T. (2020). Ten-year evolution on credit risk research: a Systematic Literature Review approach and discussion. Ingeniería e Investigación, 40(2), 50-71.
[4] Moro, S., Cortez, P., & Rita, P. (2016). An automated literature analysis on data mining applications to credit risk assessment. Artificial Intelligence in Financial Markets: Cutting Edge Applications for Risk Management, Portfolio Optimization and Economics, 161-177.
[5] Metawa, N., & Metawa, S. (2021). Internet Financial Risk Early Warning Based on Big Data Analysis. American Journal of Business and Operations Research, 3(1), 48-60.
[6] Salman, A. O., & Mohammed, M. A. (2021). Customer churn prediction using sandpiper optimization with bidirectional gated recurrent unit for business intelligence. American Journal of Business and Operations Research, 6(1), 36-47.
[7] Protopapadakis, E., Niklis, D., Doumpos, M., Doulamis, A., & Zopounidis, C. (2019). Sample selection algorithms for credit risk modelling through data mining techniques. International Journal of Data Mining, Modelling and Management, 11(2), 103-128.
[8] Zhu, Y., Xie, C., Sun, B., Wang, G. J., & Yan, X. G. (2016). Predicting China’s SME credit risk in supply chain financing by logistic regression, artificial neural network and hybrid models. Sustainability, 8(5), 433.
[9] Attaran, M., & Deb, P. (2018). Machine learning: the new'big thing'for competitive advantage. International Journal of Knowledge Engineering and Data Mining , 5(4), 277-305.
[10] Fitzpatrick, T., & Mues, C. (2016). An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market. European Journal of Operational Research, 249(2), 427-439.
[11] Sun, T. (2019). Applying deep learning to audit procedures: An illustrative framework. Accounting Horizons, 33(3), 89-109.
