Is Artificial Intelligence a positive variable in detecting financial fraud?
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Financial fraud has become an important challenge for banks, financial institutions, businesses, and consumers. As financial transactions increasingly move online, fraudsters have developed more sophisticated methods of stealing money and manipulating financial systems. Traditional fraud detection systems often rely on predefined rules, while artificial intelligence can analyze large amounts of transaction data and identify unusual patterns. This research examines whether artificial intelligence is genuinely helpful in detecting financial fraud. The study uses secondary evidence from recent academic research and systematic literature reviews concerning artificial intelligence, machine learning, and financial fraud detection. The findings indicate that AI can improve fraud detection by identifying complex patterns, processing large volumes of transactions, and adapting to certain types of fraudulent behavior. Machine-learning techniques such as random forests, gradient-boosting models, neural networks, and anomaly-detection methods have demonstrated strong performance in different fraud-detection applications. However, AI is not a perfect solution. Fraud datasets are often highly imbalanced, fraudulent behavior changes over time, and some advanced AI models are difficult to explain. False positives can also create problems by incorrectly flagging legitimate transactions. Therefore, the evidence suggests that AI is a useful tool for fraud detection, but its effectiveness depends on data quality, model design, continuous monitoring, and human oversight. AI is most appropriately viewed as an enhancement to fraud-detection systems rather than a complete replacement for human investigators and traditional controls.
Keywords
Anomaly Detection, Artificial Intelligence, Banking, Cybersecurity, Financial Crime, Financial Fraud, Fraud Detection, Machine Learning