International Research Journal of Engineering and Technology (IRJET) Volume: 11 Issue: 04 | Apr 2024
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e-ISSN: 2395-0056 p-ISSN: 2395-0072
ENHANCING ANOMALY DETECTION IN FINANCIAL DATA THROUGH MACHINE LEARNING TECHNIQUES 1Ms. R. Poorani, 2Dr. J. Ramya 1 PG Scholar, Department of ECE, Hindusthan College of Engineering and Technology, Coimbatore.
2 Associate Professor, Department of ECE, Hindusthan College of Engineering and Technology, Coimbatore.
------------------------------------------------------------------------***------------------------------------------------------------------------made a significant impact is anomaly detection. Anomalies, Abstract - Financial markets generate vast volumes of
or deviations from expected patterns, often carry crucial insights, and their early detection can prove to be invaluable for decision-makers. This journal focuses on the fascinating and highly practical realm of "Anomaly Detection," a process of identifying and flagging unusual patterns, outliers, or irregularities within complex datasets. Anomalies, depending on the context, can signify opportunities, threats, inefficiencies, or errors. Whether in the context of finance, healthcare, cybersecurity, manufacturing, or any other field, the ability to identify and respond to anomalies is indispensable. Machine learning, with its power to handle vast datasets and learn complex patterns, has emerged as a game-changer in anomaly detection. Leveraging a diverse array of algorithms and approaches, machine learning equips us with the means to sift through massive data streams, pinpoint deviations, and distinguish meaningful anomalies from random noise. The synergy of data and algorithms opens doors to a multitude of use cases, from fraud detection and quality control to predictive maintenance and cybersecurity.
data, and within this data, anomalies or abnormal patterns can hold critical information for investors, regulators, and financial institutions. Identifying these anomalies is crucial for risk assessment, fraud detection, and informed decisionmaking. Leveraging the power of machine learning, this project presents an approach to detect anomalies in financial data. The project begins with the collection and preprocessing of financial data from various sources, ensuring data cleanliness and reliability. Exploratory data analysis techniques are applied to gain insights and engineer relevant features. Different anomaly detection techniques, including statistical methods and machine learning algorithms, are employed to identify deviations from normal behavior. The model's training phase involves data splitting and the selection of an appropriate anomaly detection algorithm. Threshold determination, which plays a vital role in detecting anomalies, is addressed, and the integration of domain knowledge is emphasized. Real-time monitoring systems are considered for applications that require continuous anomaly detection in streaming financial data. Case studies and real-world examples demonstrate how anomaly detection can benefit financial institutions by highlighting fraudulent activities, market irregularities, or unusual trading behaviors. The project also delves into the challenges and limitations associated with anomaly detection in financial data, along with strategies for model refinement. As transparency and interpretability become increasingly important, the project discusses approaches to explain model results to stakeholders. Additionally, it touches upon the compliance and regulatory considerations relevant to financial data handling, including data privacy and security concerns. In conclusion, this project showcases the value of leveraging machine learning for anomaly detection in financial data. Identifying and addressing abnormal patterns, contributes to more informed decision-making, reduced risks, and enhanced security in the complex world of finance.
II. Supervised and Unsupervised Machine Learning Techniques for Anomaly Detection Machine learning, as a study of algorithms to learn regular patterns in data and their taxonomy, unnaturally includes supervised, unsupervised, and semi-supervised types. Supervised literacy assumes labeled data cases to learn from, which declares a model’s wanted affair. Depending on the algorithm in use, a model is acclimated to prognosticate classes or numerical values within a range. The supervised literacy type is the most common. The top difference in unsupervised literacy is the absence of markers that still can live in the original data but that aren't taken into account as a point by this type of model. Affair from unsupervised models is grounded on the test data-related parcels understanding and findings, and the main thing is to prize data structure rather than specific classes. This includes distance and viscosity-grounded clustering as a system to group data cases into bones with analogous parcels. Association rules mining also belongs to the ultimate type. The selection of a machine literacy model type for anomaly discovery depends on the vacuity of markers and being conditions for inferring unknown patterns. Anomaly discovery with supervised machine
KeyWords: Anomaly Detection, Financial Systems, Fraud Detection, Machine Learning.
I. Introduction In today's data-driven world, the role of data analysis and machine learning has become pivotal across various domains. One area where these technologies have
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