Leveraging artificial intelligence for real-time fraud detection in financial transactions: A fintech perspective
Independent Researcher.
Research Article
World Journal of Advanced Research and Reviews, 2019, 03(03), 115-127
Article DOI: 10.30574/wjarr.2019.3.3.0129
Publication history:
Received on 13 october 2019; Revised 25 october 2019; accepted on 29 october 2019
Abstract:
This paper focuses on using artificial intelligence to identify fraud in the financial technology (fintech) industry in real time. With the advancement in financial transactions through digital means, fraud detection is an important aspect that should not be lacking. But rule-based systems, statistical analysis, and manual reviewing have set the stage for fraud detection. However, they have failed to deliver effectively to follow the increasing and more organized and clever forms of fraud activities. This paper seeks to fill this gap by exploring some benefits associated with AI in handling fraud as a technique that provides accuracy, real-time analysis, and efficiency in detecting and preventing fraud. Implementing artificial intelligence technologies, including machine learning, deep learning, and natural language processing, offers an excellent opportunity for financial organizations to safeguard their customers and their transactions. With the help of machine learning techniques, it is possible to determine suspicious transactions within a large cube, which would be impossible to achieve through conventional practices. Deep learning models can enhance this analysis as they can accurately process complex data structures.
NLP takes these capabilities further by leveraging text data from transactions and adding further layers of security regarding sentiment and entity analysis. The paper focuses on the study of modern approaches in fraud detection and shows the benefits of AI solutions based on comparison. It points towards the importance of real-time analysis in fraud detection. For instance, identifying and discontinuing fraud cases is very crucial at that particular time. These claims are supported by real-life examples and existing literature, which point to the fact that the integration of AI has the practical purpose of improving security and efficiency. For instance, the financial institutions that implement AI-based fraud detection systems have experienced a drop in both false positives and false negatives, increasing the reliability of fraud detection. In addition, automation of the fraud detection processes has been identified to have reduced costs greatly in manual efforts, thereby decreasing the overall operating costs.
Keywords:
Artificial Intelligence; Fraud Detection; Fintech; Real-Time Analysis; Financial Transactions; Machine Learning
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Copyright © 2019 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0