Work

Anomaly Detection in Trading Data Using Machine Learning Techniques

Year: 2024

Type: article

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.

Source: International Journal For Multidisciplinary Research

DOI: 10.36948/ijfmr.2024.v06i04.26288

Authors: Ms. Shravani Prabhutendolkar -, Mr. Abhihjeet Swami -, Mr. Saurabh Vidhate -, Mrs. Vaishali Deshmukh -, Ms. Snehal Pate -, Rupali Kharat -

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4401756495

FWCI: 0.304

Cites: 2

Cited by: 2

Related to: 10