Enhancing Financial Data Security with Early Machine Learning Models

Authors

Mahaboobsubani Shaik

Abstract

The following research investigates the application of ML techniques in enhancing the security of financial data, considering the ever-evolving challenges presented by sophisticated cyber threats. It explains the development of robust ML models that are able to identify and mitigate different forms of fraud in financial transactions. Much emphasis in this study has been given to data preprocessing with feature selection, normalization, and the handling of imbalanced datasets to assure accurate and reliable model performance. These algorithms, such as decision trees, support vector machines, and neural networks, are assessed against a set of validation metrics including precision, recall, F1-score, and area under the ROC. Comparative studies reveal that ML models outperform traditional rule-based and statistical methods in finding anomaly and fraudulent activities. Empirical results indicated significant improvement in the fraud detection rate, a reduction in false positives, and quick threat identification that justifies the practical utility of ML in securing financial systems. The study further discusses challenges on model interpretability and evolving attack patterns and provides certain strategies for making systems adaptive and resilient. It will help further the creation of secure, efficient, and trustworthy financial systems through advanced analytics, as well as open up new avenues for future innovation in data protection and fraud prevention.

Keywords

Security of financial data, machine learning, fraud detection, anomaly detection, data preprocessing, validation metrics, cyber threats, secure financial systems, feature selection, neural networks, decision trees, and support vector machines

Enhancing Financial Data Security with Early Machine Learning Models. Mahaboobsubani Shaik. 2017. IJIRCT, Volume 3, Issue 1. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2412030