Comparative Analysis of the Performance of Machine Learning Models in the Prediction of Credit Risk

Aug 3, 2026

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This article delivers a comparative analysis of machine learning models for credit risk assessment, focusing on identifying approaches that maximize predictive accuracy while maintaining efficient computation. Using a historical credit dataset covering diverse borrower attributes and credit performance indicators, models such as K-Nearest Neighbors, decision trees, support vector machines, random forests, and Naive Bayes are evaluated. After data preprocessing and feature extraction, performance is measured with accuracy, precision, and recall. Results emphasize stronger default and non-performing loan detection by specific models, clarifying how variables interact to influence risk, supporting adoption by financial institutions.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Background to the Study

Literature Review

Comparative Analysis of the Performance of Machine Learning-Models in the Prediction of Credit Risk Assessment Moses A. Kolnagbayana*, Martin K. Wallaceb, Li Chowc, Darnette M. Herrond, Francis Saahe, Titus G. Goodingf, Melvin I. Soclog a,b,d,eDepartment: Computer and Information Sciences, University of Liberia cDepartment of Computer Science and Artificial Intelligence, Amity University, Jaipur gDepartment of Information Technology, The United Methodist University, Monrovia, Liberia aEmail: [kolnagbayanma@ul.edu.lr](kolnagbayanma@ul.edu.lr), [b](bEmail: wallacemk@ul.edu.lr)[Email: wallacemk@ul.edu.lr](bEmail: wallacemk@ul.edu.lr), cEmail: [li2chow@gmail.com](li2chow@gmail.com), dEmail: [herrondm@ul.edu.lr](herrondm@ul.edu.lr), eEmail: [saahf@ul.edu.lr](saahf@ul.edu.lr), [f](fEmail: goodingtg@ul.edu.lr)[Email: goodingtg@ul.edu.lr](fEmail: goodingtg@ul.edu.lr), [g](gEmail: melvinsoclo@hotmail.com)[Email: melvinsoclo@hotmail.com](gEmail: melvinsoclo@hotmail.com) Abstract This article conducts a comparative analysis of various machine learning models in predicting credit risk assessment. The study aims to discern the most effective model for enhancing accuracy and efficiency in this domain. Leveraging a comprehensive historical credit dataset with diverse borrower attributes and credit performance indicators, several machine learning algorithms, including K-Nearest Neighbors, decision trees, support vector machines, random forests, and Naive Bayes, were rigorously evaluated. Through meticulous data preprocessing and feature extraction techniques, the performance of each model was assessed using key evaluation metrics such as accuracy, precision, and recall. The findings highlight the superior predictive capabilities of certain models over others in identifying credit defaults and non-performing loans, shedding light on nuanced variable interactions influencing credit risk. This analysis serves as a valuable guide for financial institutions seeking to adopt the most effective machine learning model in their credit risk assessment processes. keywords: credit risk assessment; financial institutions; machine learning; predictive. Received: 10/16/2024 Accepted: 12/3/2024 Published: 12/16/2024 * Corresponding author. 1. Background to the Study In recent years, the landscape of credit risk assessment has witnessed a transformative shift, spurred by technological advancements and the exponential growth of financial data. Notably, the integration of machine learning and data analytics has emerged as a pivotal force reshaping this domain [1] . Prior research by [2] highlights the efficacy of machine learning models in predictive analytics, highlighting their ability to discern complex patterns within diverse datasets. Similarly, [3] emphasize the significance of machine learning techniques in enhancing predictive accuracy, particularly in the realm of judicial decision-making process. The historical evolution of credit risk assessment traces a trajectory from trust-based systems to structured methodologies. Early lending practices, as observed in ancient societies, relied heavily on interpersonal relationships and moral character assessments [4] . However, with the evolution of economies and the surging demand for credit, traditional methods gave way to more structured approaches. The emergence of credit reporting agencies, exemplified by the pivotal role played by Dun & Bradstreet in aggregating and disseminating credit data, marked a significant milestone. The advent of credit scoring models in the early 20th century, notably the introduction of the FICO score, standardized metrics that encapsulated critical credit criteria [5] . This historical evolution laid the groundwork for the contemporary paradigm shift, where machine learning and artificial intelligence are revolutionizing credit risk assessment. These advanced technologies, by leveraging vast datasets and intricate pattern analysis, have significant

FAQs

Which machine learning models are evaluated for credit risk prediction?

The study evaluates K-Nearest Neighbors, decision trees, support vector machines, random forests, and Naive Bayes, using a historical credit dataset with borrower attributes and credit performance indicators.

How is model performance measured in the article?

Model performance is assessed after data preprocessing and feature extraction using key evaluation metrics including accuracy, precision, and recall.

What is the main purpose of the comparative analysis?

The analysis aims to determine which machine learning model performs best for predicting credit risk, helping financial institutions adopt more effective lending risk assessment approaches.

Aug 3, 2026

Upload by Levi