LSTM–Based Anomaly Detection for Fraud and Financial Crime.

Abstract

Anomaly detection is crucial to financial institutions in detecting fraud and financial crime. Financial institutions are becoming more profitable because of the increasing use of cyber technology, but this has also made them more vulnerable to financial crimes and fraud.  Artificial intelligence's quick development, especially in the field of machine learning and deep learning, has offered various automatic anomaly detection models to detect anomalies better and faster than the traditional methods in fraud and financial crime; however, given the large volume of financial transactions and the challenges of identifying such transactions as fraudulent or not. Unsupervised machine learning-based anomaly detection is more effective in detecting fraud and financial crimes in the financial industry. To tackle these problems, we provide in this study an improved long short term memory (LSTM) based time-series anomaly detection method. The suggested scheme's main components include an enhanced LSTM model that might produce more accurate time-series predictions for financial crimes and fraud in financial institutions, as well as a technique for figuring out the right error threshold for anomaly detection based on prediction mistakes. To improve anomaly detection performance even more, we also suggest a pruning strategy to lower the quantity of false anomalies. The difficulty of the highly unequal distribution of financial transaction data is overcome by our approach, which dynamically establishes a threshold of prediction errors to identify anomalies instead of depending on scarce anomaly labels. We evaluated performance through a series of intensive trials in a financial transaction activity. In comparison to current anomaly detection techniques, the experimental findings show that the suggested approach performs better and is effective.