Identify, test and manage risk
Managing risk, governance, and audit compliance requires a robust platform that captures all aspects of your modeling portfolio, making it easy to inventory and document models and streamline reporting for audit and regulatory reviews.
Domino’s Enterprise MLOps platform helps quants, analysts and data scientists at the world’s largest Financial Services institutions manage risk across use cases including:
- Credit risk
- Capital planning
- Anti-money laundering
- Regulatory compliance
- Cyber risk and remediation
- Trader surveillance
- Data loss and protection
Why Domino for Model Risk Management and Governance
The Domino Enterprise MLOps Platform streamlines the work of managing risk, governance, controls, and audit processes at leading financial institutions worldwide.
Manage model risk-+
Automate and standardize processes to holistically manage risk
Domino helps you manage and streamline the end-to-end process for building a model, from idea to impact to validation while mitigating risks.
Benefits:
- Easily see relevant model management and validation workflow information.
- Centrally communicate model risk information to internal and external stakeholders.
- Codify and standardize risk, data science, IT, audit, and compliance processes.
Collaborative risk management-+
Instill best practices and build upon prior work
Domino makes it easy for teams to collaborate and build upon past work to test new ideas. It also lets you establish consistent patterns and practices to increase productivity.
Benefits:
- Collaborate and iterate on results seamlessly with analysts, data scientists, IT, risk, compliance, audit, and executives.
- Retain institutional knowledge when key people leave.
- Search prior work on a topic to avoid reinventing the wheel and rapidly compound knowledge.
- Accelerate onboarding of new team members.
- Codify consistent patterns and practices across teams.
Reproducible models and analysis-+
Easily reproduce and validate work, improving productivity
Domino lets data scientists and model validators instantly recreate the environment used to build a model or analysis.
Benefits:
- Easily validate model results prior to deployment.
- Reproduce any model with just a few clicks, regardless of the language used to create it.
- Improve data scientist and analyst productivity by automating the capture of all model assets.
Increased model velocity-+
Streamline and standardize data science processes while mitigating risks
With Domino, data scientists can develop, deploy, monitor, and manage models – all from a single platform. Models get into production faster with consistent deployment practices to rapidly respond to business needs, increase impact, and mitigate risks.
Benefits:
- Get models into production faster with consistent model deployment best practices and processes.
- Monitor models for data drift, quality, and other critical health statistics
- Iterate quickly (and often) to maintain high-performing production models to mitigate risks and losses.
- Integrate with Git and Jira to include data science into broader enterprise project processes.
Automate and standardize processes to holistically manage risk
Domino helps you manage and streamline the end-to-end process for building a model, from idea to impact to validation while mitigating risks.
Benefits:
- Easily see relevant model management and validation workflow information.
- Centrally communicate model risk information to internal and external stakeholders.
- Codify and standardize risk, data science, IT, audit, and compliance processes.
Instill best practices and build upon prior work
Domino makes it easy for teams to collaborate and build upon past work to test new ideas. It also lets you establish consistent patterns and practices to increase productivity.
Benefits:
- Collaborate and iterate on results seamlessly with analysts, data scientists, IT, risk, compliance, audit, and executives.
- Retain institutional knowledge when key people leave.
- Search prior work on a topic to avoid reinventing the wheel and rapidly compound knowledge.
- Accelerate onboarding of new team members.
- Codify consistent patterns and practices across teams.
Easily reproduce and validate work, improving productivity
Domino lets data scientists and model validators instantly recreate the environment used to build a model or analysis.
Benefits:
- Easily validate model results prior to deployment.
- Reproduce any model with just a few clicks, regardless of the language used to create it.
- Improve data scientist and analyst productivity by automating the capture of all model assets.
Streamline and standardize data science processes while mitigating risks
With Domino, data scientists can develop, deploy, monitor, and manage models – all from a single platform. Models get into production faster with consistent deployment practices to rapidly respond to business needs, increase impact, and mitigate risks.
Benefits:
- Get models into production faster with consistent model deployment best practices and processes.
- Monitor models for data drift, quality, and other critical health statistics
- Iterate quickly (and often) to maintain high-performing production models to mitigate risks and losses.
- Integrate with Git and Jira to include data science into broader enterprise project processes.




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Driving Customer Value and Efficiency by Transforming Model Development and Deployment
Learn how a 6X reduction in the time to move models into production enables Moody’s Analytics to get information into the hands of clients faster.
Read the Moody's case studyRecommended resources
"Without Domino, getting a model into production was quite an involved process; let’s say that took us maybe four weeks after talking to everyone. Now it’s down to maybe a one-week process."
Principal Consultant, Large Financial Service Organization
Domino helps Coatue deliver better models by accelerating experimentation, increasing productivity for data scientists, and reducing delays when deploying models to production while reducing operating costs. They are able to do while also insuring everything is reproducible, validated, and auditable.
Sebastien Conort from BNP Paribas Cardif shared his experience with Model Risk Governance at Rev2. He shared details about the new form of model risk governance that was needed to deal with new types of AI/ML models, the volume of models in production, and the spread of new users across the company.