Anomaly Detection in Trading Data Using Machine Learning Techniques

Author(s)	Ms. Shravani Prabhutendolkar, Mr. Abhihjeet Swami, Mr. Saurabh Vidhate, Mrs. Vaishali Deshmukh, Ms. Snehal Pate, Rupali Kharat
Country	India
Abstract	This study applies machine learning models, including Isolation Forest, DBSCAN, and Random Forest, to detect anomalies in trading data. By comparing supervised and unsupervised approaches, the research identifies effective methods for real-time detection of unusual trading activities, aiding in fraud preven-tion and market manipulation detection.
Keywords	Machine Learning, anomaly detection, DBSCAN, Trading data.
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 4, July-August 2024
Published On	2024-08-19
Cite This	Anomaly Detection in Trading Data Using Machine Learning Techniques - Ms. Shravani Prabhutendolkar, Mr. Abhihjeet Swami, Mr. Saurabh Vidhate, Mrs. Vaishali Deshmukh, Ms. Snehal Pate, Rupali Kharat - IJFMR Volume 6, Issue 4, July-August 2024. DOI 10.36948/ijfmr.2024.v06i04.26288
DOI	https://doi.org/10.36948/ijfmr.2024.v06i04.26288
Short DOI	https://doi.org/gt7m4j

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