AI presents enormous potential for financial services firms, but also introduces serious risk. From automating credit underwriting and predicting loan losses to detecting fraud in real time, AI is rapidly reshaping how banks, insurers, and asset managers operate.
For Global 2000 financial institutions, adopting AI isn’t just about leveraging high performing models and agents. It’s about building, buying, managing and deploying AI with trust, which is in line with regulatory expectations, customer demands and your organization’s risk tolerance.
At Credo AI, we’ve partnered with leading financial institutions to strategize and operationalize robust and trustworthy AI. Below, we outline the three most common barriers to trustworthy AI adoption in financial services, and what companies can do to move past them.
ROADBLOCK #1 - Confusion and Overlap Between AI Governance and Model Risk Management (MRM)
As financial institutions adopt increasingly complex AI systems, many struggle to distinguish where Model Risk Management (MRM) ends and AI Governance begins. This ambiguity creates organizational friction, redundant assessments, and compliance blind spots, ultimately slowing down AI innovation.
MRM, guided by standards like SR 11-7, is well-established for traditional statistical models, with a focus on model-level validation, performance monitoring, and traditional model risk controls. In contrast, AI Governance takes a broader view—it provides enterprise-wide oversight to ensure that all AI use cases, including those using 3rd party AI vendors and agents, are compliant with regulations and are explainable, rigorously tested, safe, and secure across the entire AI lifecycle. This includes AI systems that fall outside the traditional model definition, such as generative AI chatbots or AI agents acting on behalf of your company. MRM is a subset of AI Governance—AI Governance sets the strategic direction, and MRM executes rigorous model oversight within that framework.
Without clear separation and integration, organizations risk:
- Duplicating efforts across governance and validation teams
- Missing risks from various AI tools (e.g., third-party LLMs, AI Agents etc)
- Fragmenting inventories and oversight processes
- Failing to meet evolving regulatory expectations that require context-based AI assessments
Through our numerous engagements with organizations across the Global 2000, we've seen leading financial services firms build AI trust and seamlessly develop cohesion between MRM and AI Governance by:
Solutions to Roadblock #1
Maintain a Comprehensive AI Use case Inventory
Develop and maintain an enterprise-wide AI inventory that captures AI use cases in pilot, development, and production. This AI inventory enables holistic oversight, risk tracking, and accountability across the organization, with clear and distinct definitions for AI models, vendors, systems and use cases.
Adopt Contextual Risk Assessment Frameworks
Evaluate AI risks at the use case level, considering factors such as business purpose, data sensitivity, potential harms, and regulatory exposure. This broader lens complements traditional model-level validation by addressing the full context in which AI operates.
Ensure Policy-to-code AI governance
Create clear connections between high-level AI governance policies (e.g., fairness, explainability, human oversight) and their implementation within technical workflows. Embed these requirements into model documentation, testing protocols, and ongoing monitoring to ensure consistent enforcement. Leverage a centralized AI governance platform to track compliance with all relevant AI policies at the use case level to ensure relevant stakeholders (legal, privacy etc.) have visibility and assurance into risk management plans.
Foster Cross-Functional Collaboration
Enable structured coordination between AI governance and MRM teams through shared workflows, overlapping committee participation, and role clarity. Regularly exchange findings to break down silos and ensure aligned oversight of AI systems.
ROADBLOCK #2 - Lack of Standardized and Repeatable AI Risk Review Processes
Many financial services organizations struggle to move from ad hoc AI reviews to a structured, repeatable, and scalable AI governance process. As AI use cases increase in volume and complexity, relying on manual reviews or bespoke risk assessments quickly becomes unmanageable.
This lack of standardization results in:
- Inconsistent application of risk thresholds and controls across use cases
- Severe delays in AI use case delivery due to unclear review requirements
- Overburdened compliance and risk teams manually coordinating across silos
- Difficulty demonstrating governance maturity to regulators or auditors
As regulatory scrutiny grows and AI adoption accelerates, firms need a more robust and scalable way to operationalize AI risk governance. Global 2000s leading the pack with respect to trustworthy AI adoption have adopted the following:
Solutions to Roadblock #2
Implement a Standardized AI Governance Workflow
Design and enforce a clear, step-by-step governance process that all AI use cases must follow from intake through approval and ongoing monitoring. Standardize decision criteria and documentation across departments to streamline adoption and reduce ambiguity.
Define Clear Roles and Ownership Across the AI Use Case Lifecycle
Map responsibilities for risk review, policy enforcement, model validation, and ongoing monitoring to specific stakeholder roles (e.g., business owners, risk teams, MRM, compliance etc.). This reduces handoff friction and ensures accountability.
Enable Real-Time Dashboards and Audit Trails
Track review status, risk and control plans, and policy compliance across all AI use cases in a centralized governance platform, such as Credo AI. This provides real-time visibility to all AI stakeholders and creates a ready-made audit trail for internal or external assurance.
ROADBLOCK #3 - Regulatory Ambiguity and Compliance Pressure
As regulatory scrutiny and standards development of AI accelerates globally, financial services firms face mounting pressure to ensure that their AI systems comply with a growing patchwork of evolving, and often ambiguous, regulations and standards. From the EU AI Act, NIST Framework, and ISO 42001, to guidance from the SEC, FTC, and prudential banking regulators, institutions must navigate an increasingly complex and fast-moving compliance landscape.
The challenge isn’t just compliance, it’s also uncertainty. Many regulations are quite complex and difficult to interpret, making it challenging for organizations to translate legal expectations into operational policies and controls. This regulatory ambiguity often leads to:
- Inconsistent regulatory interpretations across business units or jurisdictions
- Difficulty demonstrating compliance during audits or supervisory exams
- Reactive risk management, where controls are added only after regulator feedback or enforcement actions
To proactively manage this landscape, institutions must build flexible and adaptive AI governance processes that balance innovation with defensibility.
Global 2000s leading the pack with respect to trustworthy AI adoption have adopted the following:
Solutions to Roadblock #3
Develop a Governance Framework Supported by Leading Standards
Establish a robust AI governance framework grounded in internationally recognized standards and regulatory guidance. Rather than creating bespoke policies in isolation, anchor your framework in the best practices provided by leading authorities such as:
- NIST AI Risk Management Framework (AI RMF): Use its core functions, such as Map, Measure, Manage, and Govern, as a structural backbone to evaluate and mitigate AI-related risks in a context-driven and repeatable way, at the AI use case level.
- ISO/IEC 42001: Incorporate this first-of-its-kind AI Management System (AIMS) standard to define organizational roles, lifecycle processes, and continuous improvement practices for governing AI responsibly at scale. It's a good procedural standard for starting out, but needs more technical implementation accomplishing AI governance.
- EU AI Act: Proactively adopt its risk-tiering model (minimal, limited, high, and prohibited risk) and required safeguards for high-risk systems, including human oversight, robustness, data quality, documentation, and post-market monitoring.
Embed Audit-Readiness into Governance Workflows
Build governance processes with transparency, traceability, and documentation at their core. Ensure every AI use case is accompanied by:
- A documented risk assessment
- Evidence of policy adherence (e.g., explainability, fairness testing)
- Clearly assigned owners and review dates
- Review and approval logs
This provides a defensible record for regulators and supports smoother audits or supervisory reviews.
Design for Global Alignment, Local Flexibility
Where possible, standardize core governance principles across jurisdictions—but allow for local tailoring based on specific regulatory requirements (e.g., GDPR in the EU, sector-specific rules in the U.S.). This ensures consistency without ignoring local nuances.
Conduct Regular Gap Assessments
Periodically assess your AI governance program against emerging regulations and frameworks. Identify areas where current processes fall short, and prioritize remediation activities. This ensures continuous improvement and reduces compliance risk over time.
From Roadblocks to Readiness
Overcoming the top roadblocks to trustworthy AI adoption isn’t just about risk mitigation—it’s about unlocking AI’s full potential in financial services. Governance, when done right, becomes a strategic asset: enabling faster innovation, smoother regulatory alignment, and greater customer trust.
By moving from fragmented oversight to structured, forward-looking AI governance, financial institutions can turn today’s roadblocks into tomorrow’s competitive advantage.
Learn how Mastercard is activating enterprise AI Governance
Check out the Mastercard case study here.
DISCLAIMER. The information we provide here is for informational purposes only and is not intended in any way to represent legal advice or a legal opinion that you can rely on. It is your sole responsibility to consult an attorney to resolve any legal issues related to this information.
