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Integrating Customer Transaction Behavior into Predictive Analytics for Improved Credit Risk Management in Retail Banking

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  • Ehimare Ucheoma Austie

Abstract

The traditional credit models used by retail banking have most likely ignored dynamic customer transaction behaviors, especially with increasing digital transactions. This suboptimal digital transaction behavior analysis thesis explores behavioral data with some degree of predictive analytics to improve credit risk management, thus enabling efficient and timely lending decisions. This paper, besides the attendant behavioral data from credit scoring and the Basel Accord and IMF frameworks, machine learning studies from Capgemini and behavioral. Data in Finance defined the operates model of risk assessment which computers and s, organization will develop an the transaction analytics behavioral aiming, developed an design thesis implement assessment synthesizing construct 120 experts from financial. The paper outlines 3 synthesis tables assessing decision readiness, the transaction risk integrated model profitability and adoption barriers. The paper proposes step-wise adoption frameworks, while also evidencing improve predictive behavior with high framework governance the central analytics focus, accuracy, reduced default rates, better yield from machine learning portfolio and generated returns predictive models transaction governance noise, by computational behavioral analytics.

Suggested Citation

  • Ehimare Ucheoma Austie, 2022. "Integrating Customer Transaction Behavior into Predictive Analytics for Improved Credit Risk Management in Retail Banking," International Journal of Scientific Research and Modern Technology, Prasu Publications, vol. 1(12), pages 172-176.
    • Handle: RePEc:daw:ijsrmt:v:1:y:2022:i:12:p:172-176:id:879
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    References listed on IDEAS

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    1. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
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