Anomaly Detection in Trading Data Using Machine Learning Techniques

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.

Authors

Publication Details

Journal
International Journal For Multidisciplinary Research
Published
2024-08-19
DOI
https://doi.org/10.36948/ijfmr.2024.v06i04.26288
Citations
2
Primary Topic
Stock Market Forecasting Methods
Type
article
Field-Weighted Citation Impact
0.98
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article

Anomaly Detection in Trading Data Using Machine Learning Techniques

Mr. Saurabh Vidhate -, Mr. Abhihjeet Swami -, Rupali Kharat -, Ms. Shravani Prabhutendolkar - et al.
2 citations
International Journal For Multidisciplinary Research
Stock Market Forecasting Methods
0.98
article

Anomaly Detection in Trading Data Using Machine Learning Techniques

Mr. Saurabh Vidhate -, Mr. Abhihjeet Swami -, Rupali Kharat -, Ms. Shravani Prabhutendolkar -, Mrs. Vaishali Deshmukh -, Ms. Snehal Pate -
article en
2 citations

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.

International Journal For Multidisciplinary ResearchVol. 6(4)
Peace, Justice and strong institutions
Openalex Percentile: Top 22%
Stock Market Forecasting Methods
0.98
DOIOpen Access PDFOpenAlex
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Anomaly Detection in Trading Data Using Machine Learning Techniques — Mr. Saurabh Vidhate -, Mr. Abhihjeet Swami -, et al. · International Journal For Multidisciplinary Research (2024) | TGRS Research Map | TGRS