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
The Problem
Financial institutions face structural limitations:
- Models based on rigid rules
- "Black box" AI that is difficult to explain
- Low capacity to detect new cases
- Limited labeled fraud data
- Complex and multidimensional data
Result:
- Too many false positives
- Lack of confidence in decisions
- Operational and regulatory risks
The Solution
ScalFraud introduces a causal approach to fraud.
The Projector™ platform enables you to:
- Identify the causes of fraudulent behavior
- Combine observed data with business expertise
- Generate realistic synthetic fraud profiles
- Select the truly determining variables
- Simulate the impact of different factors
Each model is:
- Explainable
- Interpretable
- Usable by compliance teams
What You Can Do
With ScalFraud, you can:
- Detect emerging fraud patterns
- Improve alert precision
- Significantly reduce false positives
- Prioritize high-value cases
- Strengthen the effectiveness of fraud teams
Results
The models developed enable:
- A significant improvement in detection performance
- Better generalization to unknown cases
- A reduction in operational costs related to false positives
Above all:
- You understand why an alert is triggered
- You strengthen internal and regulatory trust
- You accelerate decision-making
Unlike Traditional Approaches
- You don’t just detect → you explain
- You don’t undergo models → you master them
- You don’t react → you anticipate
Frequently Asked Questions
Causal fraud detection
ScalFraud models the cause-and-effect relationships behind fraudulent behavior, enabling detection of emerging patterns that rule-based systems and black-box AI miss.
Reduce false positives at scale
By understanding the true causes of fraud, ScalFraud dramatically reduces false positive rates — freeing compliance teams to focus on high-risk cases and reducing operational costs.
AML and regulatory compliance
Generate explainable, auditable fraud detection results that meet AML and CFT regulatory requirements. Strengthen your compliance posture with transparent, traceable decision logic.
