Simulate the impact of new controls or regulations
Strengthen resilience against emerging risks
Use Cases
Banking use case for moving from fraud detection to fraud understanding: Causal analysis of fraudulent behaviors
Classical approaches detect signals after the fact. ScalFraud models cause-and-effect relationships to understand fraud mechanisms and anticipate risks before they materialize.
- Test detection scenarios on rules and behaviors
- Identify the factors that truly cause fraud
- Reduce false positives and improve alert precision
Insurance use case for anticipating and controlling fraud: Causal analysis of claims and behaviors
Traditional approaches rely on rules or correlations. ScalFraud models cause-and-effect relationships to reveal causal fraud mechanisms, simulate scenarios, and secure decisions in complex environments.
- Reveal the causal mechanisms behind fraud
- Simulate fraud scenarios to test the impact of control measures
- Improve alert reliability and decision quality
