Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Financial fraud poses a significant threat to the stability and integrity of financial systems worldwide. Detecting fraudulent activities in financial transactions has become a critical challenge for financial institutions. This paper explores the application of data mining techniques for fraud detection in financial transactions. The objective is to leverage advanced analytics and machine learning algorithms to enhance the accuracy and efficiency of fraud detection systems. The study employs a comprehensive dataset to train and evaluate various data mining models, presenting results through tables to highlight the effectiveness of the proposed approach.

Keywords- Fraud Detection, Data Mining, Financial Transactions, Machine Learning, Decision Trees, Neural Networks, Ensemble Methods, Preprocessing, Model Evaluation, Precision-Recall Trade-off.