International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 06 | Jun 2025
p-ISSN: 2395-0072
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Leveraging Microservices for Fraud Detection and Prevention in Fintech: An AI-Driven Perspective Sumit Bhatnagar Independent Researcher, New Jersey, USA ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The rise in fraudulent activities across the
environment. Efficient fraud detection is essential for safeguarding financial assets and maintaining the trust and reliability of digital financial services. [1-2] The integration of state-of-the-art ML and AI technology with fintech is setting the stage for a revolutionary change in the area of fraud detection. These technologies guarantee a significant improvement in the ability to detect and prevent fraudulent acts by analyzing large datasets in realtime, recognizing complex patterns, and accurately anticipating fraudulent transactions. Implementing fraud detection systems powered by artificial intelligence is one way to decrease the possibility of financial crimes and establish a safe space for transactions, which keeps users loyal to fintech platforms. The use of AI and ML in the identification of fraud is not without its challenges, though. At one end of the spectrum are ethical considerations regarding data privacy, and at the other end of the spectrum are technical obstacles, such as regulating algorithmic biases and the need for massive processing resources. Furthermore, AI systems need to be able to identify new fraudulent schemes as they come up and constantly adjust to these approaches if they are to be effective in avoiding financial fraud. Consequently, preventing financial fraud and maintaining satisfied and confident clients are two of the most important reasons why fraud detection inside the fintech ecosystem is so important. The utilization of complex technological interventions is a must for digital financial transactions. This introductory section lays the groundwork for a comprehensive examination of how artificial intelligence and machine learning have transformed fintech fraud detection, thereby resolving one of the most pressing issues in the era of digital finance.
banking, insurance, governmental, and law enforcement sectors has heightened the need for robust fraud detection and prevention systems. This paper explores an AI-driven approach to combat credit card-not-present (CNP) fraud, a prevalent form of fraud characterized by the unauthorized use of card details for online transactions. Given the increasing sophistication of fraud attempts, it is essential to implement effective measures that protect sensitive personal information. We present a comprehensive framework known as the Credit Card Fraud Detection and Prevention (CCFDP) system, which integrates big data analytics to identify and mitigate fraudulent activities. The CCFDP encompasses both the Fraud Detection Process (FDP) and the Fraud Prevention Process (FPP), with the FDP focusing on detecting suspicious behaviors and the FPP aiming to thwart potential fraud before it occurs. To enhance the efficacy of the FDP, Logistic Regression Learning (LRL), and Random Under Sampling (RU). Ensuring a balanced dataset is crucial, which is achieved through random under sampling. Additionally, to improve data organization and reduce dimensionality. In the FPP, the LRL model is utilized to predict the likelihood of successful or unsuccessful transactions, thereby enabling proactive measures against CNP fraud. The proposed CCFDP framework is implemented using Python, demonstrating its applicability in real-world fintech environments. This research aims to provide insights into effective strategies for enhancing fraud detection and prevention capabilities, ultimately contributing to a more secure financial landscape. Key Words: CCFDP, logistic regression learning, PCA, LRL, fraud prevention
Enhancing Fraud Detection with Artificial Intelligence Fintech platforms have proliferated in the digital age, ushering in a new era of remarkably quick financial transactions. Financial fraud has grown increasingly prevalent and sophisticated alongside the expansion of digital financial services, posing severe dangers to their security and integrity. Despite these challenges, AI has emerged as a revolutionary tool for enhancing fintech fraud detection. The ways in which fraud is detected and prevented have been revolutionized by the introduction of artificial intelligence (AI) tools like deep learning (DL) and ML. The use of AI has greatly improved the efficiency and accuracy with which fintech companies can analyze large
1.INTRODUCTION The Importance of Detecting Fraud in Financial Technology All throughout the globe, people's perspectives, behaviors, and expectations surrounding financial transactions have been transformed by the fintech revolution, which brought about a digital transformation in the financial services industry. Despite enhancing accessibility, efficiency, and innovation, financial institutions are today more vulnerable to complicated forms of fraud. Having reliable fraud detection tools is of the utmost importance in this dynamic
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