|Milliman Complex Risk Analysis (CRisALIS)|
Risk is complex and difficult to predict. Make the right strategic decisions and investments to protect your business with Milliman CRisALIS, providing tailored data selection, advanced modeling, a robust dashboard, and AI that predicts how risks will change over time.
Cyber risk is complex and dynamic
The interconnectedness of people and systems makes cyber risk challenging to predict and manage. Attackers constantly evolve their methods in an attempt to outthink your defenses. The potential for insider threats and errors adds to the uncertainty. Traditional risk quantification approaches such as assessments, loss distribution analysis, scenario analysis, and frequency/severity modeling don’t solve the business problem on their own.
CRisALIS use cases
- Cyber. Cyber is an adversarial risk – someone is trying to outthink you.
- Liquidity. Cyber is a new trigger for liquidity risk, CFPs and RRPs.
- TPRM/Vendor. Assessments and risk registers are inadequate.
- Conduct. FI’s struggle with defining conduct, let alone measuring it.
- Non-financial risk/Operational. Standard approaches are lacking for modeling complexity.
CRisALIS key features
- Intelligent data selection
- The solution leverages both qualitative and quantitative data to enhance the credibility of the model and adapts to new information as it becomes available. We identify the most meaningful data for your organization, separating signal from noise whether that means augmenting, narrowing, or cleansing what you already have.
- Holistic view
- Variables are considered in relationship to one another, rather than in isolation, providing a more realistic view of the complex nature of cyber risk and the ability to bring unforeseen consequences to light.
- Support for key processes
- The insights delivered by CRisALIS help simplify CVA analysis and strategic planning. Interactive dashboards enable “what-if” scenario exploration to drive effective risk management.
- Clarity about how uncertainty drives risk
- We quantify the degree of certainty embodied in various assumptions. These effects are clearly shown in an intuitive user interface, enabling deeper understanding of risk.