Optimizing financial fraud detection models using genetic algorithms

Title

Optimizing financial fraud detection models using genetic algorithms

Creator

Srivastava, Shilpa; Gupta, Varuna; Mehndiratta, Vandana; Tripathi, Aprna

Description

In the contemporary financial environment, financial deception is a persistent challenge that results in significant economic losses annually. Using machine learning models to detect fraud has become an essential instrument for financial institutions to mitigate these risks. Nevertheless, the optimization of these models to achieve a balance between efficiency and accuracy continues to be a significant obstacle. In this chapter, the application of Genetic Algorithm (GA) as a potent optimization technique for improving financial fraud detection models is examined. Inspired by natural selection, GAs provide a unique way to addressing complicated optimization problems by iteratively improving a population of solutions. The chapter commences by providing a brief summary of financial detection and the limitations associated with conventional approaches. It then explores the fundamental concepts of GAs, including selection, crossover, mutation, and fitness evaluation, to provide a comprehensive understanding of how GAs may be used to improve fraud detection systems. In an exhaustive methodological section, we explore the actual use of GAs to optimize different model parameters, such as feature selection and hyperparameter tweaking. The analysis shows that GA-optimized models outperform standard approaches in terms of detection accuracy, false-positive rate, and computing efficiency. 2025 selection and editorial matter, Sulabh Bansal, Aprna Tripathi, Shilpa Srivastava and Prem Prakash Vuppuluri; individual chapters, the contributors.

Source

Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems;pp.360-380

Date

01-01-2025

Publisher

CRC Press

Coverage

Srivastava S., Christ University, Bengaluru, India; Gupta V., Christ University, Bengaluru, India; Mehndiratta V., Christ University, Bengaluru, India; Tripathi A., Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 978-104034487-3; 978-103277087-1;

Format

online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1201/9781003612858-15

https://www.scopus.com/pages/publications/105012011694?origin=resultslist