Fraud detection is usually manual and labor-intensive. At scale, it can be a tricky problem to tackle. Learn how RapidMiner helped this organization apply AI to identify $20M of fraud.
- Healthcare fraud can be challenging to detect
- Even for intelligent medical providers & patients
- Fraudsters well versed in remaining undetected
- Using a manual process to catch fraud wasn’t working
- Random samples selected for inspection
- Less than 5% of all transactions were inspected
- Time-consuming & limited resources
- Focused on specific behavioral patterns
- Easily prototyped & tested effectiveness of ML
- Supervised learning deployed:
- Scan and flag high-risk fraud cases
- In high volume, in real-time
- Integrated a variety of new sources:
- Detection of fraud networks
- Contextual clues that surround fraudulent acts
- $20M Fraud identified
- New end-to-end detection & prevention process
- Able to identify and prioritize high-risk cases
- Reduced time wasted with random inspections
- Detect new fraud patterns
- New categories of fraud discovered
Get started with your fraud detection project today!
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