Transforming Risk Management for the Age of AI.

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A Deloitte 2024 survey found that 78% of financial services institutions are already using AI in some capacity, yet only 35% have formal AI governance frameworks in place. That's not a competitive advantage. That's regulatory risk waiting to materialize. 

A 2025 MIT report titled "The GenAI Divide: State of AI in Business 2025", shared a brutal assessment of the current AI landscape. Key Findings & Statistics:

• The 95% Failure Rate: The study found that 95% of AI pilots fail to move beyond the pilot phase or show any measurable impact on the company's profit and loss (P&L).
• The Investment Gap: Organizations are heavily skewed toward technology over human capital - investing 93% in technology and only 7% on people (employee training, change management, and workflow redesign).

Federal financial regulators made their position clear in a 2024 Government Accountability Office report: "Financial institutions must manage AI use in a safe, sound, and fair manner, in accordance with applicable laws and regulations."

Regulators are applying existing frameworks (SR11-7, EU AI Act, GLBA, SR13-19 etc.) to AI systems and uses.

Common AI/ML use cases across banks and other financial services:

• Credit & Lending: Credit scoring models, loan decisioning, risk rating engines, portfolio analytics
• Operations & Compliance: Fraud detection, transaction monitoring, AML screening, identity verification, cybersecurity threat detection
• Customer Experience: Chatbots and virtual assistants, personalized recommendations, call routing, sentiment analysis
• Investment & Trading: Portfolio optimization, algorithmic trading, market analysis, robo-advisory platforms
• Insurance: Underwriting automation, claims processing, risk assessment, pricing models
• Third-Party Systems: Core platforms, CRM tools, risk management software (all increasingly AI-enabled)

Strong risk management framework is critical for future growth, successful AI implementation, and regulatory compliance. 

Inadequate risk management is very expensive:

• Regulatory Risk: Nationstar's systemic failures in data management, internal controls, compliance monitoring, weak third-party and operational oversight, due to the rapid acquisition of large mortgage servicing portfolios resulted in CFPB violations: Nationstar Mortgage paid $73M - CFPB Consent Order 
• Model Risk: Inadequate governance and oversight of internal processes, lack of model risk management, and insufficient technology controls result in severe OCC penalties: JPMorgan's $300M Penalty - OCC Findings Summary  
• Third-Party Risk: How strong is your vendor oversight program? Do you know which third-parties are using AI in their platforms? You are responsible for identifying and managing your vendor risk: CFPB fined Experian & TransUnion $17M+ - CFPB Action 
• Fair Lending Risk: If your credit decisions (whether AI-driven or not) can't be explained or are producing discriminatory outcomes, you're looking at potential consent orders and fines: Trustmark National Bank paid $9M - DOJ Settlement 
• Competitive Risk: While you're waiting, your competitors are leveraging AI safely and effectively improving efficiency, enhancing customer experience, and making better decisions.

Institutions with mature ERM foundations and AI governance are:

• 30% faster at deploying new AI use cases (Gartner)
• 2x more likely to achieve ROI from AI investments (MIT Sloan)
• 40% more efficient in compliance processes (Forrester)
• Better positioned to attract top talent and technology partnerships

The institutions winning in AI aren't moving recklessly. They're moving confidently because they built the right foundation. Strong enterprise risk management is the foundation; AI governance strengthens your foundation and positions your organization for future success.

Federal regulators don't adjust their AI governance expectations based on your asset size. Whether you're a $5 billion community bank or a $50 billion wealth manager, if you're using AI, you're expected to govern it properly.

Challenge - You likely don't have:

• A dedicated Chief AI Risk Officer (CAIO)
• An AI governance committee
• Big Four consulting budgets
• Specialized AI risk expertise in-house

You're expected to meet the same standards as large financial institutions without the same level of resources.

Institutions that aren't using AI are at a competitive disadvantage - with proper risk management and governance, you can:

• Innovate confidently knowing you have guardrails in place
• Scale AI adoption across business lines systematically
• Satisfy regulators before they find gaps
• Attract talent who want to work with modern technology
• Serve customers better through responsible AI deployment
• Accelerate time-to-market for new AI capabilities
• Build customer trust through transparent, ethical AI use

The institutions thriving in five years won't be the ones with the most AI. They'll be the ones who implemented it responsibly - with a strong foundation of clear governance, proactive risk management, and executive-level oversight.

You can wait for regulators to tell you what to do and spend the next year rebuilding foundations, remediating findings, explaining gaps to your Board, and hiring more resources or you can get ahead of it now with a clear inventory, solid governance foundation and framework, and exam-ready documentation.

That's exactly why Elevâre exists.

Elevâre helps banks and other financial services institutions turn risk into resilience and strategy into measurable growth.