AI-Enhanced Check Fraud Detection: Transact Security Made Possible by Machine Learning
DOI:
https://doi.org/10.53555/ephijse.v7i3.297Keywords:
AI fraud detection, machine learning, check fraudAbstract
Particularly in the areas of check fraud and clearance fraud, financial fraud has gotten ever more complex and banks and other financial institutions run great danger as a result. Usually emphasizing rules, conventional fraud detection systems find it difficult to fit the continually changing fraudulent methods. Technologies created to be potent tools for the identification and prevention of fraud in real time in response to such kinds of issues are artificial intelligence (AI) and data mining (ML). These technologies improve the accuracy of spotting fraudulent transactions by way of modern methods including real-time transaction monitoring, photo recognition, and anomaly identification, therefore limiting the amount of false positives. This study examines three artificial intelligence models: XGBoost, Support Vector Machines (SVMs), and convolutional neural networks (CNNs). This paper aims to assess the identification of fraudulent behavior inside the check processing system by means of these models. Image-based fraud detection that is, the use of check images and signatures to identify forgeries, changes, and other anomalies depends on conventional neural networks (CNNs). This helps to lower false alert counts and enhance fraud detection powers.SVMs and XGBoost are also rather useful in spotting suspicious trends in transaction data by means of supervised learning approaches. This paper looks at a well-known American bank that effectively used artificial intelligence-driven fraud detection to reduce the traditional review time by 60% and the deception count by 48%. More importantly, the results of this research provide clear advantages of applying artificial intelligence-based fraud detection techniques. Technology, financial institutions might greatly improve their capacity in the areas of client confidence building, operational efficiency enhancement, and fraud detection improvement by using machine learning also known as artificial intelligence.
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