Leveraging Machine Learning and Data Science for Real-Time Fraud Detection in Financial Markets.

EasyChair Preprint 15098

Abstract

The growing complexity of financial markets, coupled with the increasing volume of transactions, has heightened the risk of fraudulent activities. Traditional methods of fraud detection, reliant on rule-based systems, often fail to keep pace with the evolving tactics of fraudsters. This paper explores the integration of machine learning (ML) and data science techniques to enhance real-time fraud detection in financial markets. By utilizing large datasets, machine learning algorithms can identify patterns, anomalies, and outliers that are indicative of fraudulent behavior, enabling institutions to act quickly and mitigate risks. We examine key ML techniques such as supervised and unsupervised learning, and explore how advanced models, including deep learning and ensemble methods, provide greater accuracy in fraud detection.

Keyphrases: Earth, Education, Environmental, financial risk, science

@booklet{EasyChair:15098,
  author    = {Oluwaseun Abiade},
  title     = {Leveraging Machine Learning and Data Science for Real-Time Fraud Detection in Financial Markets.},
  howpublished = {EasyChair Preprint 15098},
  year      = {EasyChair, 2024}}