Prediction and Analysis of Financial Crises Using Machine Learning

View Sample PDF

Author(s): S. Baranidharan (CHRIST University (Deemed), India)and Harishchandra Singh Rathod (Shri Jairambhai Patel Institute of Business Management, India)
Copyright: 2023
Pages: 15
Source title: Advancement in Business Analytics Tools for Higher Financial Performance
Source Author(s)/Editor(s): Reza Gharoie Ahangar (Lewis University, USA)and Mark Napier (Lewis University, USA)
DOI: 10.4018/978-1-6684-8386-2.ch010

Keywords: Accounting and Finance / Business Science Reference / Data Analysis & Statistics / Library & Information Science

Purchase

View Prediction and Analysis of Financial Crises Using Machine Learning on the publisher's website for pricing and purchasing information.

Abstract

This study presents a comparative analysis of various machine learning algorithms for credit risk assessment. The algorithms were tested on two credit datasets: German Credit Dataset and Australian Credit Dataset. The performance of the algorithms was evaluated based on several metrics, including sensitivity, specificity, accuracy, F-score, and Kappa. The results showed that the FCPFS-QDNN algorithm outperformed other algorithms in both datasets, achieving high accuracy, sensitivity, specificity, and F-score. On the other hand, the ACO Algorithm and Multilayer Perceptron algorithms were found to perform poorly in both datasets. The findings of this study have significant implications for credit risk assessment in banking and financial institutions. The study recommends the use of the FCPFS-QDNN algorithm for credit risk assessment due to its superior performance compared to other algorithms.

The IRMA Community

Research IRM

Prediction and Analysis of Financial Crises Using Machine Learning

Purchase

Abstract

Related Content

IRMA Sponsors