Model Risk Management Lifecycle

Manage and monitor models across users and lifecycle stages

Model Risk Management

MATLAB for Model Risk Management provides unified and integrated tools that interoperate with your data, systems, and third-party products at every touchpoint of the model lifecycle. Using MATLAB, novice users to experienced coders can:

  • Capture repeatable workflows through code generation and document linking
  • Automate testing and validation for continuous monitoring
  • Scale algorithms, models, and apps both horizontally and vertically
  • Focus on issues across the lifecycle with full model lineage and usage reporting

Use MATLAB throughout the Model Risk Lifecycle

MATLAB Model Risk Management platform consists of six fully customizable components that support the management of data and models across the lifecycle. Each component supports integration with existing tools and infrastructure, from desktop to cloud. All lifecycle stages are synchronized through a centralized model inventory that tracks the full model lineage and use.

Model Inventory and Repository (MIR)

Manage models and modeling projects

  • Provide centralized access to models
  • Manage model validation projects
  • Inspect models, intermediate results, and audit trail

Stage 1: Model Development Environment (MDE)

Define and develop

  • Explore, develop, back-test, and document models and methodologies
  • Improve transparency and reproducibility of model development
  • Auto-generate model documentation and reporting

Stage 2: Model Review Environment (MRE)

Review and approve

  • Perform independent model reviews on the complete set of model artifacts
  • Interactively perform sensitivity analysis on model parameters
  • Comment and flag any aspects for response and resolution

Stage 3: Model Test and Validation Environment (MTVE)

Perform quality assurance and validation

  • Provide the environment for approved models to undergo preproduction testing and validation
  • Automatically run unit tests and generate test reports
  • Compare tests of a preproduction model against the currently deployed production model

Stage 4: Model Execution Environment (MEE)

Implement and deploy models

  • Host production models and scale to end users in a secure controlled environment
  • Deploy models onto a production environment without translation
  • Integrate with existing technology infrastructures

Stage 5: Model Monitoring Dashboard (MMD)

Monitor, report, and assess

  • Summarize model execution results using a configurable web dashboard
  • Explore data segments and configure alerts and threshold for automated monitoring