Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Srivastava, Shilpa; Gupta, Varuna; Mehndiratta, Vandana; Tripathi, Aprna Title A name given to the resource Optimizing financial fraud detection models using genetic algorithms Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems;pp.360-380 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1201/9781003612858-15" target="_blank" rel="noreferrer noopener">https://doi.org/10.1201/9781003612858-15</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012011694?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012011694?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant 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 Description An account of the resource 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. Publisher An entity responsible for making the resource available CRC Press Relation A related resource ISBN: 978-104034487-3; 978-103277087-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online