Summary of Enhancing Financial Fraud Detection with Human-in-the-loop Feedback and Feedback Propagation, by Prashank Kadam
Enhancing Financial Fraud Detection with Human-in-the-Loop Feedback and Feedback Propagation
by Prashank Kadam
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Engineering, Finance, and Science (cs.CE)
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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 This paper investigates the effectiveness of Human-in-the-Loop (HITL) feedback mechanisms in enhancing machine learning models for financial fraud detection. By incorporating feedback from Subject Matter Experts (SMEs), even small amounts of input can significantly boost model performance, particularly with graph-based techniques that benefit the most. The study uses proprietary and publicly available datasets to demonstrate how HITL feedback improves model accuracy and introduces a novel feedback propagation method that extends feedback across the dataset, further enhancing detection accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about using human help to make machine learning models better at detecting financial fraud. Fraud patterns are always changing, so it’s hard to keep up without getting some expert input. When we get just a little bit of feedback from experts, our models can become way more accurate. The researchers looked at different ways of giving feedback and found that using special graph-based techniques works best. They also came up with a new way to share feedback across the data that makes it even better. |
Keywords
* Artificial intelligence * Machine learning
