A Model for Anomaly Detection in Financial Transactions using Hybrid Machine Learning Technique

Abstract

Anomaly detection is essential in applications such as financial fraud detection, network security, and system health monitoring. This study presents a hybrid anomaly detection model for financial transactions, combining the strengths of both unsupervised and supervised learning techniques—Isolation Forest and Random Forest, respectively. the Isolation Forest algorithm, performs unsupervised detection of outliers, effectively identifying rare or irregular data points without requiring labeled input. The detected anomalies are then used to construct a pseudo-labeled dataset, which is subsequently used to train a Random Forest classifier. The Random Forest model learns discriminative patterns between normal and anomalous data, enhancing overall detection capabilities. The system is implemented in Python using Scikit-learn for model development and evaluation. Experiments on benchmark financial transaction "Credit Card Fraud Detection Dataset from Kaggle" The hybrid model achieved a precision of 99.7%, recall of near 100%, and an F1-score of 99.98%, outperforming standalone algorithm The model also improved robustness to noise and better scalability for large datasets. Keywords: Anomaly Detection, Financial Fraud, Hybrid Machine Learning, Supervised Learning, Security, Unsupervised Learning. CISDI Journal Reference Format Azubuike Nnendah Daisy; Anireh, Vincent Ike-Emeka & Sako, D.J.S. (2025): A Model for Anomaly Detection in Financial Transactions using Hybrid Machine Learning Technique. Computing, Information Systems, Development Informatics and Allied Research Journal. Vol 16 No 3, Pp 37-50 Available online at www.isteams.net/cisdijournal. dx.doi.org/10.22624/AIMS/CISDI/V16N3P3