AI-driven Automation of Business rules: Implications on both Analysis and Design Processes

Abstract

The fast development of Artificial Intelligence (AI) has transformed the way organizations automate their decision-making and deal with complex business rules. Business rules have traditionally been designed and maintained manually, and developed inflexible systems that cannot readily cope with the evolving environment. This study discusses the application of AI methods to automation of business rules and its consequences to business analysis, credit management, and system design. The research utilizes the Home Equity Loan (HMEQ) data set, which contains financial and demographic information about loan applicants to come up with predictive models that enable simulation of the automated processes of loan approval and risk assessment. The main aim is to build a data-driven analytical model, which is able to determine the clients who have the highest chances of defaulting on loans. With respect to the Equal Credit Opportunity Act (ECOA), the research focuses on the ethical, interpretable, and statistically sound credit score models. A quantitative analytical method was used involving the use of Python to preprocess data and statistical modeling as well as Tableau to visualize data. The important variables such as the amount of loan, the value of property, type of job, existing debt, and delinquency were examined to reveal the trends in the loan repayment results. The results indicated that there were strong correlations between loan purpose and employment stability as well as between loan default and employment stability. There were greater risks of non-recovery on borrowers who took loans in order to consolidate their debts and those with a lesser employment term whereas applicants in the professional/ executive category demonstrated better repayment patterns. This study offers an understandable, clear, and effective credit risk evaluation system to the financial institutions by combining AI-based modeling experience with visual analytics. The results improve the accuracy of the automated lending systems as well as the promotion of fairness, accountability, and informed decision making. In the end, the research exhibits the disruptive nature of AI-powered analytics in contemporary finance and justifies the inclusion of predictive intelligence in moral and responsible credit management.