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Research Article Open access CC BY 4.0

Securing Tomorrow: The Intersection of AI, Data, and Analytics in Fraud Prevention

Pankaj Gupta

Asian Journal of Research in Computer Science · pp. 75–92 · Published 5 Feb 2024

10.9734/ajrcos/2024/v17i3425

Abstract

Aim: This research investigates the interconnections among Data Analytics, Artificial Intelligence, and other cutting-edge technologies to enhance comprehension of fraud prevention. The advantages of integrating machine learning and data analytics into artificial intelligence systems for industry-wide fraud detection and prevention are examined in this study. Study Design: My approach involved conducting an extensive examination of existing literature and analysing numerous case studies to gather information on the role of artificial intelligence, data, and analytics in fraud prevention. Place and Duration of the Study: A broad spectrum of academic, corporate, and governmental sources is utilised to supply the research study with its international scope. This study examines publications and developments from 2019 to 2023. Methodology: The research procedure incorporated an exhaustive literature review. This assessment was composed of academic journals, conference proceedings, and official publications. A qualitative analysis was conducted to assess the data, identify commonalities, and evaluate the strengths and weaknesses of AI fraud protection solutions. A more comprehensive examination of practical implementations was facilitated by case studies, which enhanced comprehension of fraud prevention strategies propelled by AI. Results: The research revealed important findings concerning the various ways in which analytics, data, and artificial intelligence can be implemented to prevent fraudulent activities. An examination of comparisons between generative AI for social engineering, credit card analytics, and cyber-physical security for Internet of Things (IoT) networks illuminated the merits and demerits of different Artificial Intelligence (AI) approaches. Conclusion: According to the findings of the study, AI, data, and analytics may alter system defences against fraud. The above-mentioned results underscore the significance of flexible fraud prevention strategies. Constant collaboration, innovative technology, and ongoing investigation are required to remain ahead of evolving fraud techniques. The paper concludes by emphasising the significance of future challenges and orientations.

Artificial intelligence data analytics fraud prevention financial data analysis java machine learning military applications real-time data engineering

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