Managing Large AI Models in Banking

The Three Pillars of AI Governance

Effective AI governance is built on the synergy of three key components that complement each other: 

• AI lifecycle management,
• risk assessment,
• and regulatory compliance.

To meet compliance standards, banks must implement appropriate processes throughout the AI lifecycle and conduct regular risk assessments. AI lifecycle management represents the “action space”—the creation, deployment, and monitoring of AI models. Risk assessment provides the “control perspective,” helping to identify potential threats associated with the use of AI technologies. Compliance, in turn, is the “goal to achieve”—ensuring that all activities align with current regulations and ethical standards. 

AI governance also demands interdisciplinary collaboration. It is not solely the domain of IT or compliance departments, but rather a shared responsibility across various teams within an organization. Cooperation between business units, technical teams, risk management, legal, and compliance departments is essential, as each brings a unique perspective to the table. Managing the AI model lifecycle should be approached like product management—with careful attention during development, as well as ongoing monitoring and adaptation. The traditional approach, where AI models were treated as one-off projects and forgotten after deployment, is no longer sufficient. 

AI governance promotes a product-oriented mindset, where AI models are seen as living systems that require continuous oversight and evolution. Compliance plays a crucial role here. If it is perceived merely as a “brake,” teams may be tempted to bypass regulations. Instead, the compliance function should help shape AI frameworks, supporting technical and business teams in understanding regulatory and ethical requirements. Krzysztof Goworek
CEO, TUATARA

What Are Large AI Models?

Large models—also known as foundation models (LxM – Large X Models) or large language models (LLMs)—are powerful AI systems trained on vast datasets. Popular examples include GPT-4, BERT, PaLM 2, LLaMA 2, and Mistral. Unlike traditional AI models designed for narrow, task-specific purposes (such as credit risk prediction), foundation models offer broad applicability and can perform a wide range of tasks—from natural language processing and document analysis to translation and text generation. 

Key Characteristics of Large AI Models

• Multitasking Capabilities: Large models can be applied across many use cases, including banking chatbots, report generation, customer sentiment analysis, and automated loan application processing.
• Often Provided by External Vendors: Due to the immense cost and infrastructure demands, banks rarely train large models from scratch. Instead, they rely on models developed by organizations like OpenAI, Google, or IBM watsonx.
• Contextual Performance Evaluation: The effectiveness of these models is not assessed in general terms, but in the context of specific applications—for example, how well a chatbot answers customer inquiries.
• New Evaluation Methods: Traditional AI metrics such as accuracy and precision are often insufficient. New metrics are needed, such as human-rated response quality (e.g., RLHF – Reinforcement Learning from Human Feedback).
• Complexity and “Black Box” Nature: These models often function as “black boxes” (like GPT-4), making them difficult to fully interpret. This necessitates the use of new explainability techniques, such as prompt impact analysis and prompt tracing.

AI Governance in Banking – How to Manage Large AI Models

Deploying large AI models in the banking sector requires a new approach to AI governance—one that ensures compliance with regulations, ethical standards, and data security. In the traditional AI lifecycle, the process includes defining the business need, building and testing the model, deploying it, and monitoring its performance. With foundation models, however, this lifecycle follows a different path: 

• Selecting a Foundation Model: Instead of building a model from scratch, the bank chooses an appropriate pre-trained model.
• Regulatory Compliance Review: The model is evaluated for compliance with regulations such as the EU AI Act and GDPR, and the provider is assessed for security and governance standards.
• Model Customization: This includes fine-tuning with bank-specific data or prompt engineering to tailor the model’s behavior.
• Approval and Deployment: The model undergoes qualitative testing and validation before final implementation.
• Performance Monitoring: Continuous evaluation of the model’s behavior in real time helps detect quality issues or potential AI hallucinations.
• Updates and Retirement: Modifications are made when the model’s performance degrades, regulatory requirements change, or if the model needs to be decommissioned.

Banks must also prioritize data security. Using public models via APIs (e.g., OpenAI) may pose risks related to customer data protection. One solution is hosting models in secure on-premise environments or working with trusted vendors who offer strong security assurances—such as IBM watsonx. 

Real-World Applications of Large AI Models in Banking

Large AI models are already transforming core banking operations. Below are some practical examples of how they are being used across the financial sector: 

• Chatbots and Customer Support: LLM-powered AI can handle customer inquiries, offering instant responses around the clock. However, these systems require mechanisms to validate the accuracy of generated answers—such as quality scoring and human-in-the-loop reviews.
• Document Analysis: Foundation models can automatically extract data from contracts and loan applications, significantly accelerating business processes and reducing manual effort.
• Risk Monitoring and Fraud Detection:AI can process large volumes of transaction data in real time, identifying anomalies and patterns that may indicate fraudulent behavior.
• Support for Analysts and Financial Advisors: LLMs assist in summarizing reports, generating insights, and analyzing market trends, allowing advisors to make faster and more informed decisions.

Managing large AI models in banking is a complex challenge that requires the integration of AI governance strategy, quality monitoring, and rigorous data security. Foundation models offer groundbreaking capabilities, but they also demand new control mechanisms and adjustments to the traditional AI lifecycle. 

The Three Dimensions of AI Governance in Practice

AI Governance is not just a theoretical concept—it requires concrete tools and processes to ensure full control over AI systems throughout their lifecycle. In practice, effective AI management in an organization relies on three key pillars:

• Model Inventory and Lifecycle Tracking: Ensuring full visibility and oversight of all AI models used within the organization.
• Model Risk Management: Identifying, assessing, and monitoring potential risks associated with AI implementation.
• Model Evaluation and Monitoring: Continuously tracking model performance, ensuring alignment with business goals, and verifying compliance with applicable regulations.

Each of these dimensions plays a vital role in responsible AI governance, enabling organizations to implement AI safely, transparently, and effectively. 

Model Inventory and Lifecycle Tracking

Effective AI model lifecycle management begins with comprehensive inventory. Organizations implementing AI must maintain a centralized registry of all models, including their versions, statuses, and update history. In practice, this involves: 

• Automatic Model Registration: AI governance systems should collect and store key metadata for each model—such as name, version, creator, deployment date, and modification history.
• Model Approval Workflows: Dedicated tools allow model teams and risk departments to review and formally approve models before deployment, ensuring transparency and accountability.
• Lifecycle Monitoring: AI models are not static—they evolve alongside the data they process. AI governance platforms should automatically flag models that require review, retraining, or retirement, based on performance or data drift.

AI Model Risk Management

Model risk assessment is a fundamental pillar of AI governance, enabling organizations to identify and mitigate potential threats associated with artificial intelligence. In practice, effective model risk management includes: 

• Model Risk Categorization: Not all AI models carry the same level of risk. They should be classified based on complexity, business impact, and regulatory exposure (e.g., under the EU AI Act). High-impact models—such as those influencing credit decisions—require stricter controls.
• AI Incident Tracking: Any event where a model performs unexpectedly—such as making biased predictions or incorrect credit approvals—must be logged and analyzed to prevent recurrence and ensure accountability.
• Integration with Enterprise Risk Management Systems: AI governance platforms are often integrated with enterprise GRC (Governance, Risk & Compliance) tools like IBM OpenPages, ensuring a unified approach to risk oversight across the organization.

Model Evaluation and Monitoring

The final pillar of effective AI governance is the continuous monitoring and evaluation of AI model performance. Since models learn from data that changes over time, their effectiveness can degrade without regular oversight. Implementing robust evaluation mechanisms ensures models stay aligned with real-world conditions. 

In practice, this involves: 

• Real-Time Model Performance Monitoring: AI governance systems track key metrics such as prediction accuracy, data drift, and performance across different customer segments.
• Bias Detection and Mitigation: AI models may unintentionally favor or disadvantage certain groups. Automated tests help identify such biases and recommend corrective actions to ensure fairness.
• Alerts and Automated Notifications: When a model’s performance falls below predefined thresholds (e.g., AUC drops below 85%), the system triggers alerts to responsible teams, suggesting retraining or modification.
• Prediction Tracking and Explainability: Organizations must have tools in place to trace and explain AI decisions—for instance, understanding why a particular loan application was rejected by the model.

AI Governance in Banking

AI Governance is a key element for the safe and effective implementation of artificial intelligence within organizations. In practice, this involves combining model lifecycle monitoring, risk management, and continuous evaluation of AI model performance. 

With the right tools, such as watsonx.governance, organizations can automate and streamline these processes, ensuring compliance with regulations, reducing risk, and optimizing AI model performance. As a result, AI becomes more transparent, ethical, and predictable—crucial for building trust with both customers and regulators.

If you’re looking for an experienced partner to help align your organization with AI Governance requirements, contact us today!