Anomaly Detection in Transactions Using Machine Learning

Abstract

Finding anomalies in financial transactions is a crucial task for spotting odd or perhaps fraudulent activity. This study offers a thorough Transaction Anomaly Detection system that efficiently detects questionable financial transactions by applying Random Forest classification and rule-based analysis. To build a strong detection framework, the suggested method incorporates feature engineering techniques, such as advanced scaling methods and transaction amount difference calculation. With carefully chosen features including transaction amount, transaction frequency, and comparative metrics, the solution makes use of scikit-learns Random Forest Classifier. The system uses a hybrid detection methodology that enables nuanced transaction analysis by fusing predefined anomalous rules with machine learning prediction. Standard Scaler for feature normalization and deliberate train-test splits are important preprocessing techniques that guarantee model generalizability. Transaction amount ratios, frequency thresholds, and comparative statistical analysis are some of the criteria that the detection algorithm uses to assess transactions. Real-time transaction review is made possible via an interactive command-line interface, which gives users comprehensive information about any irregularities and particular justifications for reporting suspicious activity. The model's ability to recognize odd transaction patterns in a range of financial situations is demonstrated by experimental validation. The study highlights the potential of machine learning to improve financial security and fraud prevention systems by offering a versatile, interpretable method of anomaly identification that is readily adaptable to various financial monitoring situations.