Summary of Proactive Fraud Defense: Machine Learning’s Evolving Role in Protecting Against Online Fraud, by Md Kamrul Hasan Chy
Proactive Fraud Defense: Machine Learning’s Evolving Role in Protecting Against Online Fraud
by Md Kamrul Hasan Chy
First submitted to arxiv on: 26 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the role of machine learning in addressing challenges in online fraud detection and prevention. It highlights the strengths of key models like Random Forest, Neural Networks, and Gradient Boosting in processing vast datasets, identifying intricate fraud patterns, and providing real-time predictions for proactive fraud prevention. Unlike rule-based systems that react after fraud has occurred, machine learning models continuously learn from new data, adapting to emerging fraud schemes and reducing false positives. The paper emphasizes the potential of machine learning to revolutionize fraud detection frameworks by making them more dynamic, efficient, and capable of handling the growing complexity of fraud across various industries. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research shows how machine learning can help stop online fraud. It uses powerful models like Random Forest and Neural Networks to analyze big data and find patterns that signal fraud. These models are better than old systems because they learn from new information and can adapt quickly to changing fraud tactics. This means less money is lost due to false positives, and businesses can stay ahead of scammers. |
Keywords
» Artificial intelligence » Boosting » Machine learning » Random forest
