Interpretable Machine Learning Models for Credit Risk Assessment in Financial Institutions

Interpretable Machine Learning Models for Credit Risk Assessment in Financial Institutions Ugochukwu Ukeje1* Abstract Credit risk assessment remains a cornerstone of decision making in the financial sector, directly influencing loan approvals, capital allocation, and systemic risk management. As machine learning models increasingly replace traditional techniques like logistic regression to enhance predictive accuracy, concerns over their […]

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Publication Details

Journal
International Journal of Science Architecture Technology and Environment
Published
2024-10-27
DOI
https://doi.org/10.63680/ijsate0324081.09
Primary Topic
Financial Distress and Bankruptcy Prediction
Type
article
Field-Weighted Citation Impact
0.00
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article

Interpretable Machine Learning Models for Credit Risk Assessment in Financial Institutions

Ugochukwu Ukeje
International Journal of Science Architecture Technology and Environment
Financial Distress and Bankruptcy Prediction
article

Interpretable Machine Learning Models for Credit Risk Assessment in Financial Institutions

Ugochukwu Ukeje
article en

Abstract

Interpretable Machine Learning Models for Credit Risk Assessment in Financial Institutions Ugochukwu Ukeje1* Abstract Credit risk assessment remains a cornerstone of decision making in the financial sector, directly influencing loan approvals, capital allocation, and systemic risk management. As machine learning models increasingly replace traditional techniques like logistic regression to enhance predictive accuracy, concerns over their […]

International Journal of Science Architecture Technology and Environment
Decent work and economic growth
Openalex Percentile: Top 48%
Financial Distress and Bankruptcy Prediction
DOIOpen Access PDFOpenAlex
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Interpretable Machine Learning Models for Credit Risk Assessment in Financial Institutions — Ugochukwu Ukeje · International Journal of Science Architecture Technology and Environment (2024) | TGRS Research Map | TGRS