Adjust models based on scenarios and constraints
Improve decision-making on credit, performance, or risk
Use Cases
Banking use case for assessing and managing credit risk: Causal scoring and decision-making
Classical approaches predict default without explaining its causes. ScalRisk models cause-and-effect relationships to understand risk mechanisms and inform decisions before they are made.
- Test 'what-if' scenarios on scoring variables and economic conditions
- Identify the factors that truly drive default risk
- Reduce bias and improve the robustness of credit decisions
Industrial use case for assessing and managing operational risks: Causal analysis of system performance
Classical approaches identify correlations without explaining the underlying mechanisms. ScalRisk models cause-and-effect relationships to understand the factors that degrade or improve performance and anticipate the impact of decisions.
- Assess the impact of industrial parameters and production constraints
- Identify key factors influencing performance, quality, or failures
- Reduce operational risks and optimize critical industrial decisions
The Problem
Risk teams face a structural trade-off:
- Complex models that are high-performing but opaque
- Simple models that are explainable but imprecise
- Inability to explain decisions
- Assumption of variable independence
- Biases linked to hidden factors (e.g., sector)
- Missing or incomplete data
Result:
- Decisions difficult to justify
- Biases in risk assessment
- Regulatory compliance limitations
The Solution
ScalRisk introduces a causal approach to credit risk.
The Projector™ platform enables you to:
- Model the cause-and-effect relationships of default
- Identify the truly determining factors
- Detect hidden biases in models
- Simulate scenarios with missing data
- Conduct robust stress tests
Each model is:
- Explainable
- Verifiable
- Compliant with regulatory requirements
What You Can Do
With ScalRisk, you can:
- Understand the causes of individual risk
- Improve your credit granting decisions
- Reduce unjustified rejections
- Identify systemic risks
- Implement early warning alerts at portfolio level
Results
The models developed enable:
- Performance equivalent to "black box" models
- Full transparency of decisions
- Greater robustness against imperfect data
Above all:
- You align performance and explainability
- You secure your decisions in the face of regulators
- You improve the fairness of models
Unlike Traditional Approaches
- You don’t just predict → you explain
- You don’t choose between performance and transparency → you have both
- You don’t suffer from biases → you identify and correct them
Frequently Asked Questions
Explainable credit and risk scoring
ScalRisk models the causal mechanisms behind credit and risk scores, identifying the factors that truly determine outcomes and enabling your teams to understand and challenge every decision.
Bias detection and model robustness
Identify hidden biases in your scoring models, detect spurious correlations, and build more robust models that generalize to new cases and withstand regulatory scrutiny.
Regulatory compliance and auditability
Produce scores and decisions that are fully explainable to regulators. ScalRisk generates audit-ready outputs aligned with explainability requirements across credit and risk frameworks.
