The impact of mathematics, statistics and AI on fraud measurement
summary
Evelyn Torezone and Bryan Prado describe how mathematics, statistics and AI are used at Appmax to measure and combat fraud, presenting an antifraud model and an applied success case. They explain the technical approach combining statistical methods and machine learning to improve fraud detection and measurement.
AI-written summary · about
chapters (10)
- 0:00Introduction
- 2:31Digital fraud problem
- 5:58Antifraud in practice
- 7:00Approach strategies
- 9:47Behavioral profile analysis
- 12:39AI over rules
- 14:04AI relearning and adaptation
- 15:29Antifraud micromodels
- 16:12Customization and scalability
- 17:36Conclusion
related talks
41mUsing Machine Learning to Fight Fraud
08mAI-Powered Fraud Detection in Financial System
45mAnti-Fraud in Reverse and the Use of ML Methods in It
16mLeveraging ML to Create a Reliable Anti-Fraud System
35mAI Powered Fraud Detection for Payments & Transactions
26mAI-Powered Fraud Detection in Cloud-Native Digital Payments
13mHarnessing Machine Learning to Combat E-commerce Fraud
39mFraud Detection: Using ML to Identify and Manage Fraudulent Activities