Authors :
Jayanth Kande
Volume/Issue :
RISEM–2025
Google Scholar :
https://tinyurl.com/2xuurkxr
Scribd :
https://tinyurl.com/5c3v8wc2
DOI :
https://doi.org/10.38124/ijisrt/25jun176
Abstract :
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].
Keywords :
Anomaly Detection, Machine Learning, Financial Transactions, Fraud Detection, Operational Efficiency.
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].
Keywords :
Anomaly Detection, Machine Learning, Financial Transactions, Fraud Detection, Operational Efficiency.
