LOAN STACKING DETECTION: LEVERAGING MACHINE LEARNING AND AI TO MITIGATE RISKS IN THE SMALL BUSINESS LOAN INDUSTRY

Authors

  • Vijay Arpudaraj Antonyraj Data & Analytics, Direct Consumer Solutions, Equifax, Equifax, Inc, United States

Keywords:

Loan Stacking, Fraud Detection, Small Business Loan Industry, Machine Learning, Risk Scoring, Credit Monitoring, Data Sharing.

Abstract

The term ‘Loan Stacking’ refers to the practice of securing multiple business or personal loans simultaneously from multiple lending companies. This strategy of acquiring increased capital in the short term has been on the rise among businesses seeking increased capital, specifically after the widened compliance restrictions in the banking financial institutions due to the 2008 recession. Following the 2008 fallout, stringent rules have been defined by the regulators to sanction loans to businesses and individuals by working closely with Credit bureaus to have the updated financial health available closer to real time prior to approving requested loans. This journal focuses on the issues faced by the financial institutions due to the rise of loan stacking, currently available detection techniques, and use cases to identify the impact of Loan Stacking in the current financial market. It also discusses the proposed advanced machine learning and AI technologies to early detect fraudulent practices, Risk Assessment Enhancements, and real-time integration with Credit Bureaus with Financial Lenders.

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References

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Published

2024-12-04

How to Cite

[1]
Vijay Arpudaraj Antonyraj, “LOAN STACKING DETECTION: LEVERAGING MACHINE LEARNING AND AI TO MITIGATE RISKS IN THE SMALL BUSINESS LOAN INDUSTRY”, IEJRD - International Multidisciplinary Journal, vol. 9, no. 2, p. 9, Dec. 2024.