JOURNAL ARTICLE

Predicting Credit Outlook of Banking and Nonbanking Finance Companies: A Comparative Analysis of Machine Learning Models.

  • Published In: Journal of Financial Data Science, 2024, v. 6, n. 3. P. 214 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Malhotra, Rashmi; Malhotra, D. K.; Malhotra, Kunal 3 of 3

Abstract

This article presents a comparative study on forecasting the credit outlook of banking and nonbanking finance companies using five distinct machine learning models: Logistics regression, k-nearest neighbor (kNN), decision tree classifier, support vector machines (SVM), and naïve Bayes classifier. The research evaluates the efficacy of these models in categorizing financial companies based on their credit outlook (negative, neutral, and positive). Our findings reveal that the kNN model with a k value of 7 demonstrated superior performance in terms of test accuracy compared to the other models. Although models such as the kernelized SVM and logistic regression exhibited commendable performance, none could match the high-test accuracy achieved by the kNN model. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Journal of Financial Data Science. 2024/07, Vol. 6, Issue 3, p214
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2024
  • ISSN:2640-3943
  • DOI:10.3905/jfds.2024.1.158
  • Accession Number:179072226
  • Copyright Statement:Copyright of Journal of Financial Data Science is the property of With Intelligence Limited and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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