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Using Agentic AI in Model Risk Management to Automate Challenger Model Development for Banks
Using Agentic AI in Model Risk Management to Automate Challenger Model Development for Banks

August 6, 2025

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In the fast-paced world of financial services, Model Risk Management (MRM) has evolved from a niche compliance function into a cornerstone of institutional stability and competitive advantage.

Financial institutions rely on a comprehensive set of models for everything from credit scoring and fraud detection to risk assessment and regulatory reporting. However, the very complexity that makes these models powerful also introduces significant risk. As mandated by regulations like SR 11-7, a robust MRM framework is non-negotiable, and a critical component of this framework is the development and use of challenger models.

Traditionally, the process of developing these challenger models—essential for validating the performance and assumptions of primary "champion" models—has been a slow, resource-intensive ordeal. It’s a manual process, often siloed, that struggles to keep pace with market volatility and the rapid evolution of AI/ML-based models.

But a new paradigm is emerging. Agentic AI, a more advanced and autonomous form of artificial intelligence, is set to transform this landscape, promising to automate the entire challenger model development lifecycle and enhance MRM coordination for unparalleled efficiency and compliance.

For financial institutions looking to stay ahead, understanding this shift is crucial. As outlined in the insights on Model Risk Management (MRM) in Banking and Finance, managing model risk is a top priority, and leveraging new technology is key to building a resilient framework.

Why Challenger Models Are So Hard to Build Today

In a typical scenario, a bank’s quantitative team might spend months developing a single challenger model. The process is linear and labor-intensive, beginning with analysts manually gathering, cleaning, and preprocessing vast datasets from disparate sources—a time-consuming task prone to human error. Next, domain experts manually engineer and select features to improve model performance—an effort that blends science with intuition and often consumes significant time.

Modeling teams then experiment with a limited range of familiar algorithms, potentially missing out on more advanced or better-suited approaches. Once a model is built, an independent team is tasked with validation—conducting back-testing, sensitivity analysis, and stress testing. Meanwhile, the documentation required for auditors and regulators is typically compiled manually, increasing the risk of inconsistency and delay.

This traditional approach introduces substantial operational friction. It drains resources, slows down model validation cycles, and delays the deployment of improved models—ultimately limiting the institution’s responsiveness and profitability. The growing complexity of AI/ML models only adds to the challenge, as their “black box” nature makes conventional validation even more difficult.

Agentic AI Changes the Game

So, what is Agentic AI? Unlike traditional AI, which typically requires human guidance to perform specific tasks, agentic AI systems are composed of autonomous "agents" that can reason, plan, and execute complex, multi-step tasks to achieve a specific goal. Think of a highly specialized, coordinated team of digital experts. For MRM, this means you can deploy an "agent crew" to handle the entire challenger model development process from start to finish.

This is a core component of the strategic digital-led transformation sweeping the financial services industry, where AI, data, and platforms converge to drive innovation.

How Agentic AI Builds Challenger Models End-to-End

Imagine deploying a team of AI agents with specialized roles to build a challenger model for your bank's mortgage default risk champion model. The process could for exmaple look like this—

  1. The Data Sourcing Agent

    This agent is tasked with identifying and accessing all relevant data sources, including internal databases, external economic indicators, and alternative datasets. It autonomously cleans, preprocesses, and unifies the data, ensuring it's ready for modeling.

  2.  

    The Feature Engineering Agent
    Analyzing the prepared data, this agent autonomously generates and tests thousands of potential features, identifying the most predictive variables far more comprehensively than a human team ever could.

  3. The Algorithm Selection Agent

    This agent explores a vast library of modeling algorithms, from logistic regression to complex neural networks. It runs a competitive tournament, training and evaluating hundreds of model variations to identify the top-performing candidates based on predefined metrics.

  4.  

    The Model Validation Agent
    This agent takes the leading candidates and subjects them to rigorous, automated validation routines. It performs back-testing against historical data, runs stress tests under various economic scenarios, and assesses for biases.

  5. The Documentation Agent 

    As the other agents work, this agent meticulously records every step of the process—data lineage, feature selection logic, model parameters, and validation results. It then automatically generates comprehensive, audit-ready documentation that meets SR 11-7 standards, creating a transparent and fully traceable audit trail.

This automated workflow drastically reduces development time from months to days, or even hours. It allows for the continuous, on-demand development of challenger models, ensuring that champion models are perpetually benchmarked against the best possible alternatives.

Beyond automation, agentic AI fosters seamless coordination. The system can act as a central hub, facilitating communication between the model development team, the validation team, and the risk management function. This aligns with the principles of effective Governance, Risk, and Compliance (GRC) in Banking, ensuring all stakeholders have real-time visibility into the model's performance and associated risks.

Furthermore, by automating compliance checks and documentation, agentic AI helps institutions adhere to the evolving regulatory landscape. This proactive approach to compliance is a core benefit of leveraging RegTech solutions in the financial services industry.

Conclusion

The adoption of agentic AI does not eliminate the need for human expertise. Instead, it elevates the role of quantitative analysts and risk officers. Freed from mundane, repetitive tasks, they can transition from being model builders to model strategists. Their focus will shift to defining the business problem, setting the strategic goals for the AI agents, interpreting the results, and making the final, critical business decisions. The human-in-the-loop remains essential for oversight, ethical considerations, and ensuring that models are not just statistically sound but also align with the bank's risk appetite and business objectives.

As institutions explore advanced technologies like Cognitive Financial Modeling, the synergy between human insight and AI-driven automation will become even more critical. By combining deep industry knowledge with expertise in AI and automation, financial institutions can build robust, efficient, and compliant MRM frameworks that fit for the future. As explored in this white paper, "Reimagining Banking Functions: How Generative and Agentic AI are Shaping the Future of Financial Services," the time to prepare for this transformation is now.


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Anaptyss is a digital solutions and business services company based in Alpharetta, GA. The organization delivers digitally enabled, value-led managed services to a diverse clientele in the financial services industry. Anaptyss co-creates innovative solutions to help clients evolve their standalone tasks and processes to fully integrated and versatile functions/CoEs, transforming their business and technology operations. Anaptyss' globally scalable managed services ecosystem, driven by the proprietary Digital Knowledge Operations™ approach, offers clients access to new-age intelligent digital technologies, deep-domain expertise, and top-tier talent.

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