NEWS DESK

New AI-Powered Experian Assistant for Model Risk Management Streamlines and Accelerates Governance Processes

Experian today announced the launch of Experian Assistant for Model Risk Management, a first-of-its-kind solution to help financial institutions govern and manage models more efficiently across the entire model‑development lifecycle. Fully integrated into the Experian Ascend Platform™ and powered by ValidMind technology, this solution helps accelerate model validation, improve auditability and transparency, and may aid financial institutions in reducing regulatory and reputational risk. This launch follows last October’s introduction of the award-winning Experian Assistant and its AI-enabled model-lifecycle features.

“Manual documentation, siloed validations and limited performance model monitoring can increase risk and slow down model deployment,” said Vijay Mehta, EVP, Global Solutions and Analytics, Experian Software Solutions. “Adhering to model-risk-management guidelines can be a tremendous strategic advantage for financial institutions when they can create, review and validate documentation quickly and at scale, and this new solution offers these capabilities.”

As financial institutions accelerate innovation, they must balance the move toward GenAI-enabled capabilities with compliance to global model-risk-management guidelines such as SR 11-7 (US) and SS1/23 (UK). Experian Assistant for Model Risk Management offers financial institutions with customizable, pre-defined templates, centralized model governance repositories, and transparent internal workflow approvals—empowering them to meet regulatory guidelines with confidence and efficiency.

“Our partnership with Experian represents a major step forward in operationalizing AI for governance and model risk management,” said ValidMind CEO Jonas Jacobi. “By embedding ValidMind’s automation and governance capabilities into the Experian Assistant for Model Risk Management, we’re helping financial institutions move faster and satisfy regulator expectations.”

“The combination of Experian’s commercial expertise and presence with ValidMind’s technology provides the foundation for scalable and explainable AI across the credit and risk lifecycle,” said Sid Dash, Chief Researcher at Chartis. “This partnership addresses a growing industry imperative – the need to establish proper AI governance that aligns with an evolving technology and regulatory environment and provides a framework for institutions to modernize their model risk practices.”

Why It Matters

  • Accelerates time-to-market by streamlining model documentation and approvals, reducing internal approval time by up to 70% and enabling financial institutions to deploy models more quickly.
  • Replacing manual processes with automation speeds up the creation, maintenance and validation of complex documents for data collection and model development.
  • Streamlines a validation team’s efforts by quickly accessing and creating consistent reports.
  • Provides the ability to monitor models with reporting insights to ensure confidence in a model’s performance and value.
  • This launch further strengthens the award-winning Experian Assistant product family, extending trusted automation and GenAI capabilities from model development into model governance.

Why Choose Experian Assistant for Model Risk Management:

  • Model-Risk-Management Excellence –Simplify model documentation efforts via automation, guided workflows and seamless tool integration.
  • Reduced Risk – Enhance consistency to better align with evolving regulatory guidelines and help mitigate the risk of compliance failures and fines.
  • Enhanced Connectivity –Access to Experian analytics experts and Ascend Ops™ for model registration and deployment, model monitoring, and scenario planning, ensures robust oversight and operational efficiency.

To learn more about Experian Assistant for Model Risk Management or schedule a demo, visit: https://www.experian.com/business/products/assistant-for-model-risk-management.

PR News Desk

PR News Desk

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