ItemConference Paper

A study of failure prediction of Indian banks using various machine learning algorithms - An examination of predictive accuracy

Title
A study of failure prediction of Indian banks using various machine learning algorithms - An examination of predictive accuracy
Creator
Ajith, Shivani; Joshi, Hemlata
Description
Banks play a key role in strengthening the economy; hence their survival is very important. It is necessary to evaluate the failure probability of banks correctly based on the factors associated with it. Over half of the assets in the financial sector in India are held by the banking sector, which holds a strong position. Phased implementation of financial sector reforms has resulted in an exciting moment of rapid transformation for Indian banks. The study here focuses on the establishment of machine learning approach to compute and compare the extent of bankruptcy based on the accuracy measure-Support Vector Machine classification, Random Forest, Logistic Regression, Nae Bayes classification using the data of 250 Indian banks having qualitative variables from 2015 to 2020. The feature selection in this paper is based on correlation and relief algorithms. The explanatory features of the dataset are drawn by implementing a two-step feature selection technique and the selected features are fed and further used for prediction using the Random Forest technique, Logistic Regression, Support Vector Machine, and Nae Bayes classification techniques. The results reveal, that the support vector machine shows a score of 99.8% forecasting the highest accuracy. This research serves as a foundation for the decisions made by a variety of stakeholders, including analysts, policymakers, shareholders, and bank management, and it facilitates the comparison of the qualitative ratios of bankruptcy. The goal is to develop a prediction system that will allow the firms and businesses to be categorized according to the level of risk. 2025 Author(s).
Source
AIP Conference Proceedings;Volume;3191;Issue;1;Article No.;40007;
Date
01-01-2025
Publisher
American Institute of Physics
Subject
Bankruptcy; Feature selection; Logistic Regression; Machine learning; Support Vector Machines
Coverage
Ajith S., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, India; Joshi H., Department of Statistics and Data Science, CHRIST (Deemed to Be University), Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 0094243X; ISBN: 978-073545110-0;
Format
online
Language
English
Type
Conference paper

Citation

Ajith, Shivani; Joshi, Hemlata, “A study of failure prediction of Indian banks using various machine learning algorithms - An examination of predictive accuracy,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25710.

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