Enhanced Banking Security: Isolation Forest with Attention Mechanism for Sophisticated Fraud Detection

Abstract

As digital banking gains appeal, the possibility of fraudulent activity grows, necessitating ongoing developments in fraud detection systems to prevent fund losses. The present study introduces a novel strategy for detecting fraud in the banking industry that uses isolation forests and attention mechanisms. The Isolation Forest (Iforest) technique detects abnormalities in huge datasets, and the Attention Mechanism improves the model's attention to essential characteristics for greater accuracy. Unlike traditional approaches, our model recognizes the underlying sequential character of banking transaction data, allowing for the exact detection of suspicious actions. Experimental assessments confirm the efficacy and efficiency of our technique, indicating a big step forward in strengthening security measures in the banking business.