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Risk Analysis of Mortgage Loan Default for Bank Customers and AI Machine Learning

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  • Ming-Tsung Hung
  • Huai-Chun Lo

Abstract

Risk Analysis of Mortgage Loan Default for Banks is a crucial issue. On one hand, it concerns the quality of the bank's credit decisions, and on the other hand, it affects the rights of homebuyers to obtain financial support. In 2008, the U.S. subprime mortgage crisis sparked a global financial meltdown. What began with the collapse of the housing market quickly spread throughout the global financial system, resulting in the failure of numerous banks and a widespread economic recession. Recently, the banking sector has increasingly leveraged Artificial Intelligence (AI) and Machine Learning (ML) to enhance decision-making processes, particularly in assessing the risk of mortgage loan defaults. This paper aims to explore the application of ML techniques to predict and analyze the risk of default among bank customers, thereby enabling financial institutions to make more accurate and informed lending decisions. Â

Suggested Citation

  • Ming-Tsung Hung & Huai-Chun Lo, 2024. "Risk Analysis of Mortgage Loan Default for Bank Customers and AI Machine Learning," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(6), pages 1-3.
    • Handle: RePEc:spt:apfiba:v:14:y:2024:i:6:f:14_6_3
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    References listed on IDEAS

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