AI-DRIVEN CREDIT CARD CUSTOMER ASSESSMENT USING BUREAU DATA

Abstract

This article explores the transformative integration of Artificial Intelligence (AI) and Machine Learning (ML) in credit card customer assessment using bureau data. With the global AI in fintech market projected to reach $46.8 billion by 2030 growing at a CAGR of 23.4%, financial institutions are increasingly leveraging advanced technologies to enhance credit decisioning processes. The article examines how AI and ML technologies improve the credit card application process through comprehensive analysis of traditional credit data, alternative data sources, and real-time behavioral patterns. The implementation of these technologies has demonstrated significant improvements across key performance metrics: a 20% increase in applicant approvals while simultaneously reducing default rates by 30%, and a dramatic reduction in processing time from 7 days to 14 seconds. Through analysis of current practices, technical implementations, and future developments, the article addresses critical challenges in data privacy, system integration, and ethical considerations while providing strategic recommendations for financial institutions implementing AI-driven customer identification processes. The findings suggest that successful integration of AI and ML can significantly improve risk assessment accuracy, operational efficiency, and customer experience in credit card issuance.