Examine ML Approaches to Identify Anomalies in Financial Transactions and Operations

Anomaly detection is essential for identifying fraudulent activities and operational discrepancies within financial systems, where the growing volume of transactions has made traditional manual methods inadequate. Machine learning (ML) techniques are a promising solution to this issue because of their ability to automatically recognize patterns that deviate from the norm. This study explores a range of supervised models, such as decision trees, support vector machines (SVM), and deep learning techniques, to identify outliers and anomalies in financial data (Bhat et al., 2015) [1]. By leveraging these models, we aim to enhance the detection process, improve accuracy, and minimize false positives compared to traditional rule-based systems (Lee et al., 2018) [2]. Supervised models can efficiently classify transactions based on labeled data, while unsupervised models are effective at detecting anomalies in unlabeled data, offering a broader range of applications in real- time systems (Zhang et al., 2019) [3]. Through extensive evaluation of different approaches, the results demonstrate that hybrid models, combining both supervised and unsupervised learning, provide the highest performance (Smith et al., 2020) [4]. The analysis shows that these models outperform existing methods in detecting novel anomalies, even those previously unseen (Goh and Xu, 2020) [5]. Machine learning in anomaly detection not only improves the efficiency of financial systems but also offers a scalable approach to big data management (Chouhan et al., 2020) [6]. The study's findings show how machine learning has the potential to transform operational efficacy and security. We also discuss the limitations of current approaches and propose new research directions, like the application of reinforcement learning and advanced ensemble techniques, to improve detection abilities in financial transactions even more (Shrestha, 2017) [7].