Deep Learning-Based Anomaly Attribution for Financial Transaction Compliance
DOI:
https://doi.org/10.15662/IJEETR.2023.0501007Keywords:
Deep Learning, Anomaly Detection, Financial Transaction Compliance, Anti-Money Laundering (AML), Know Your Customer (KYC), Fraud Detection, Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), Autoencoders, Transaction Patterns, Regulatory Compliance, Machine Learning, Suspicious Transactions, Financial Institutions, Risk AssessmentAbstract
The current paper examines the application of operational models based on deep learning to identify anomalies during financial transactions and attribution to improve the adherence costs to regulatory measures like the Anti-Money Laundering (AML) and Know Your Customer (KYC). The complex frauds usually do not work well on a traditional system of rules that could be detected in real time. Using Convolutional neural networks (CNNs), Long short-term memory networks (LSTMs), and autoencoders, deep learning models are applicable to detect and warn suspicious transactions based on the historical patterns of transactions. This paper will illustrate that deep learning-based anomaly detection will enhance detection accuracy, minimize false positives, and guarantee compliance within the banking, fintech, and credit card transactions sectors
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