A Review of AI-Driven Business Intelligence in Consumer Finance Risk Control
DOI:
https://doi.org/10.6981/FEM.202609_7(9).0006Keywords:
Artificial Intelligence; Business Intelligence; Consumer Finance Risk Control; Credit Invisible Customers; Alternative Data.Abstract
As artificial intelligence (AI) and business intelligence (BI) become more common in consumer finance, the limits of traditional credit-risk systems are becoming easier to see. Conventional models still depend heavily on credit-bureau records, so customers with little or no formal credit history can be difficult to assess. This paper uses a systematic literature review to examine how AI and BI are being used in credit-risk assessment, with particular attention to alternative data, credit-invisible customers, model explainability, and regulatory compliance. The literature indicates that combining AI with BI can improve risk identification for long-tail customer groups by using a broader range of behavioural and transaction information. However, complex models also increase the difficulty of transparency management and regulatory compliance. Based on these findings, this paper proposes an analytical framework from three dimensions: algorithm, system, and compliance. It aims to provide a reference for financial institutions seeking to optimize intelligent risk control modeling.
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