ModelRisk

ModelRisk

ModelRisk is risk management software used by banks and financial services companies to model and quantify financial risks. It helps build, validate, and audit risk models for market risk, credit risk, and operational risk.
ModelRisk image
risk-management financial-risk market-risk credit-risk operational-risk banks financial-services

ModelRisk: Risk Management Software

ModelRisk is risk management software used by banks and financial services companies to model and quantify financial risks. It helps build, validate, and audit risk models for market risk, credit risk, and operational risk.

What is ModelRisk?

ModelRisk is an enterprise risk management software platform used by banks, insurance companies, asset managers, and other financial services firms to model, measure, and manage different types of risk.

It provides a centralized platform to build, validate, document, and audit the complex financial and risk models that drive business decisions. Key capabilities include:

  • Model development workflows to standardize and govern model building
  • Tools to quantify and analyze risks like market risk, credit risk, liquidity risk, and operational risk
  • Model validation tools to evaluate conceptual soundness, ensure data quality, and backtest predictive accuracy
  • Production integration to put validated models into action across the business
  • Audit trails, reports, and visual analytics for model transparency and regulatory compliance

ModelRisk helps financial institutions increase modeling efficiency, reduce time-to-market for new models, improve model quality and trustworthiness, and demonstrate effective model governance. This enables better risk-aware decision making across lending, trading, portfolio management, treasury, and stress testing.

The software is part of SAS's risk management suite and is used predominantly by large banks and insurers to manage model risk.

ModelRisk Features

Features

  1. Monte Carlo simulation
  2. Historical simulation
  3. Sensitivity analysis
  4. Stress testing
  5. Value at Risk (VaR)
  6. Expected Shortfall
  7. Credit risk modeling
  8. Liquidity risk modeling
  9. Optimization tools

Pricing

  • Subscription-Based

Pros

Comprehensive risk analytics

Industry standard models

Customizable modeling

Automated model validation

Regulatory compliance

Cons

Expensive licensing

Steep learning curve

Requires quantitative expertise

Limited to financial services


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