AUTOMATED ANOMALY DETECTION IN FINANCIAL TECHNOLOGY: LEVERAGING ISOLATION FOREST ALGORITHM FOR REAL-TIME INSIGHTS AND OPERATIONAL STABILITY

Abstract

Anomaly detection plays a pivotal role in identifying irregularities in data, particularly in industries where operational efficiency and security are paramount. This research introduces a robust automated anomaly detection system tailored for financial technology applications. By leveraging the Isolation Forest algorithm, the system efficiently analyzed weekly deletion trends of banking applications across multiple partners. The study identified critical anomalies and provided actionable insights to prevent operational disruptions, saving over 50% of potentially deleted applications in the final quarter. Key results, visualized through detailed trends and score distributions, underscore the system’s effectiveness in real-time anomaly detection. The implementation not only enhanced detection accuracy to over 95% but also streamlined anomaly management processes, demonstrating scalability and adaptability for diverse operational needs. These findings highlight the importance of dynamic thresholds, contextual analysis, and automated systems in maintaining data integrity and operational stability.