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 Krishna E S, Anuram; Varghese James, Blesson Title A name given to the resource Digital Payments and Fraud Risk: Technological Innovations and Global Regulatory Responses Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Contributions to Finance and Accounting;Volume;Part F2026;pp.55-82 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-032-20198-0_3" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-032-20198-0_3</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105040173217?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105040173217?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 Krishna E S A., Department of Global Finance &amp; Analytics, Christ University, Pune, India; Varghese James B., Department of Commerce, Christ University, Pune, India Description An account of the resource In the digital age, financial crime has increased in magnitude and complexity because of high-velocity digital payments, cross-platform financial systems, and due to new forms of threat, like deepfakes and synthetic identities. Due to the increasing failure of the conventional, more rules-based methods of fraud control to detect these more sophisticated behaviours, there has arisen the need to adopt more sophisticated, technology-driven prevention methods. This paper discusses how financial-crime detection and AML/CFT processes are changing with the use of innovative tools, namely AI and machine learning analytics, privacy-preserving federated models, blockchain-based identity mechanism, and real-time digital-payment intelligence. The chapter methodologically follows the comparative case-based approach to analysis synthesising the evidence presented in regulatory publications, industry reports, and documented cases of technological deployments to define general trends, advantages, and shortcomings. The results indicate that technology-enabled systems are much more effective in detecting fraud, presenting few false positives, enhancing identity assurance, and sharing across institution intelligence at a faster rate as compared to traditional systems. This paper concludes that responsible AI architecture, real-time data network integration, and regulatory cooperation are essential to establishing resilient, future-ready models that will be able to defend financial ecosystems against the changing threats of the digital era. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Anti-money laundering (AML); Artificial intelligence (AI); Counter-terrorist financing (CFT); Digital fraud; Financial crime detection; Machine learning analytics Publisher An entity responsible for making the resource available Springer Nature Relation A related resource ISSN: 27306038; 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