AI Implementation Roadmap - Financial Services Edition

Overview

Financial AI implementation takes longer than other industries—and for good reason. Regulatory requirements demand validation before deployment. Fair lending testing must be completed and documented. Model risk management processes must be followed. Rushing implementation creates regulatory risk that far exceeds any time savings.

This 120-day playbook provides a realistic timeline for compliant AI deployment. It's designed for Tier 1 and Tier 2 models; lower-risk implementations may proceed faster, higher-risk may require extension.

What's Inside

• Why Financial AI Implementation Is Different: Timeline comparison showing why financial services requires 2-3x longer than other industries. Regulatory gates, validation requirements, and documentation needs that extend timelines.
• Implementation Framework: Three-phase structure with clear deliverables and decision gates

Phase 1: Foundation (Days 1-30)

• Executive alignment and governance approval
• Use case definition and success criteria
• Data assessment and readiness
• MRM engagement and validation planning
• Fair lending testing design
• Vendor evaluation (if applicable)
• Resource allocation and team formation

Phase 2: Development & Validation (Days 31-75)

• Model development or vendor configuration
• MRM validation process
• Fair lending testing execution
• Documentation development
• Security assessment
• Integration planning
• User acceptance testing

Phase 3: Deployment (Days 76-120)

• Production deployment

• Monitoring implementation

• Ongoing testing procedures

• User training

• Documentation finalization

• Examination readiness review

• Post-deployment validation

• Model Risk Management Integration: How to work with MRM throughout implementation—avoiding delays through early engagement

• Fair Lending Implementation: Testing requirements, documentation, adverse action notice preparation for AI credit decisions

• Vendor Implementation Playbook: Third-party AI implementation specifics—due diligence, validation, ongoing monitoring

• Success Metrics: How to measure implementation success in financial services context

• Budget & Resource Planning: Realistic budgets for financial services AI implementation

Who This Is For

• Chief AI Officers planning AI deployments
• Project Managers implementing financial AI
• Model Risk Management coordinating validation
• Fair Lending Officers ensuring compliance
• Technology Leaders integrating AI systems

Why This Resource

Generic implementation approaches fail in financial services—they don't account for MRM timelines, fair lending requirements, or examination readiness. This roadmap is built for regulated environments, with realistic timelines and regulatory gates built in.

Early MRM engagement guidance prevents the delays that occur when validation is treated as an afterthought.

FAQ

Q: Why 120 days when other industries deploy faster?

A: Financial services has regulatory requirements that don't exist elsewhere: SR 11-7 validation, fair lending testing, examination documentation. These take time. Shortcutting creates regulatory risk. The timeline comparison section shows why each phase requires longer.

Q: What about lower-risk AI implementations?

A: The 120-day timeline is for Tier 1/2 models per SR 11-7 classification. Lower-tier models may proceed faster, but governance gates should still be observed. Adjust timeline based on model risk tier.

Q: How do we work with MRM to avoid delays?

A: The MRM integration section provides guidance on early engagement—getting MRM involved in Phase 1, not Phase 3. Early engagement surfaces validation requirements before they become delays.