American Journal of Business and Operations Research AJBOR 2692-2967 2770-0216 10.54216/AJBOR https://www.americaspg.com/journals/show/1755 2018 2018 Faculty of Engineering and Technology agra College Agra, India Khyati Chaudhary VIPS-TC, School of engineering and technology, Delhi, India Gopal Chaudhary 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. 2023 2023 32 38 10.54216/AJBOR.100204 https://www.americaspg.com/articleinfo/1/show/1755