Credit Scoring Model Development: Taking Advantage of Credit History

This paper introduces a novel credit scoring model that leverages extensive credit history data and advanced ma-chine learning techniques to enhance the accuracy of assessing creditworthiness. Our approach diverges from traditional credit scoring methods by integrating a logistic regression model th...

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Bibliographic Details
Main Authors: Ziaur Rahaman, Shah Md, Anika, Afra, Islam, A.K.M. Muzahidul
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Adaptation models
Credit Risk Assessment
Credit Scoring Model
Data models
Data Sources
Deep Learning
Financial Inclusion
FinTech Credit
History
Logistic regression
Machine learning
Predictive models
Soft sensors
Stability analysis
Technological innovation
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Summary:This paper introduces a novel credit scoring model that leverages extensive credit history data and advanced ma-chine learning techniques to enhance the accuracy of assessing creditworthiness. Our approach diverges from traditional credit scoring methods by integrating a logistic regression model that captures intricate patterns in borrower data, enabling more precise predictions of loan repayment probabilities. The results demonstrate that our model effectively predicts borrower defaults with a high level of accuracy, achieving an overall accuracy rate of 90.45% and a strong area under the ROC curve (ROC-AUC) score of 0.94. The model exhibits a precision of 85% and recall of 83% for identifying defaulters, reflecting a balanced ability to detect actual defaults while minimizing false positives. The specificity of the model for non-defaulters is 94%, indicating a robust performance in correctly identifying borrowers who will repay their loans. This study underscores the potential of machine learning to refine decision-making processes in financial lending by offering a quantitative tool that enhances informed credit decisions, thereby promoting financial inclusion while effectively managing risks.
ISSN:2642-6102
DOI:10.1109/TENSYMP61132.2024.10752316