Prediction and analysis of financial crises using machine learning

Title

Prediction and analysis of financial crises using machine learning

Creator

Baranidharan S.; Rathod H.S.

Description

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. 2023, IGI Global. All rights reserved.

Source

Advancement in Business Analytics Tools for Higher Financial Performance, pp. 200-214.

Date

2023-01-01

Publisher

IGI Global

Coverage

Baranidharan S., CHRIST University (Deemed), India; Rathod H.S., Shri Jairambhai Patel Institute of Business Management, India

Rights

Restricted Access

Relation

ISBN: 978-166848388-6; 1668483866; 978-166848386-2

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.4018/978-1-6684-8386-2.ch010

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171232813&doi=10.4018%2f978-1-6684-8386-2.ch010&partnerID=40&md5=dbcd436022763f602f9498bdd739e493