Year: 2025
Type: article
Abstract: Credit card transactions generate extensive data that can be used for valuable financial insights. This project analyzes Kaggle transaction data to improve fraud detection, customer segmentation and credit risk assessment. Data records are processed in Excel, followed by structured management in SQL, and visualized in Power BI using DAX expressions to derive meaningful knowledge. Fraud detection is improved by using extended statistical models and AI control models to identify suspicious transaction patterns. Combining fraud detection, customer behavior analysis and credit risk assessment in a single analytical framework, the project provides a data control approach to help financial institutions make appropriate found decisions. The interactive Power BI dashboard allows financial analysts to pursue trends, assess risks and effectively optimize their financial strategies. This approach addresses the limitations of traditional credit card analytics by providing a scalable, automated, adaptable financial information system tailored to the developing financial environment.
DOI: 10.55041/ijsrem43661
Institution: University of Mumbai
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: bronze
OpenAlex ID: W4409117876