AI Readiness Checklist for Banking, Financial Services & Insurance

Core banking system data is accessible via APIs for analytics without impacting transaction performance.

Sensitive financial data is tokenized and handled in compliance with PCI-DSS standards throughout the pipeline.

Real-time transaction data flows through a structured pipeline for fraud detection and analytics models.

A unified customer data model aggregates data from banking, investment, insurance, and interaction touchpoints.

Full data lineage tracking supports audit requirements for SOX, Basel III, and AML/KYC compliance.

Real-time market feeds and alternative data sources are integrated for risk and trading models.

Legacy data stores have been migrated to a modern cloud data warehouse with proper schema management.

Historical transaction and customer data is archived with proper retention policies and retrievability.

Cloud infrastructure meets financial regulatory requirements with proper audit controls and certifications.

Stream processing infrastructure handles real-time transaction scoring at millisecond latency.

A centralized feature store provides consistent features for fraud, risk, and credit models.

Secure API gateway manages access to AI services with rate limiting, authentication, and logging.

Models are versioned and tracked in a registry with full reproducibility for regulatory review.

AI services are deployed with HA and disaster recovery meeting financial uptime SLAs.

Model explanation frameworks (SHAP, LIME) are integrated for regulatory compliance.

Isolated test environments enable safe experimentation with production-like financial data.

Data scientists with financial modeling and quantitative analysis skills are on the team.

Team members understand AI intersection with model risk management and regulatory requirements.

Product managers translate business needs into AI requirements and manage AI product lifecycles.

An independent model validation team reviews AI models for bias, accuracy, and compliance.

C-suite executives actively champion AI initiatives and allocate dedicated resources.

Business units, IT, risk, and compliance collaborate on AI initiatives with clear ownership.

Regular upskilling programs keep teams current on AI techniques and financial applications.

MLOps engineers manage model deployment, monitoring, and lifecycle in production environments.

AI governance follows SR 11-7 model risk management guidelines with proper documentation.

A formal AI ethics policy addresses fairness, transparency, and accountability in financial decisions.

All customer-facing AI models meet explainability requirements for regulatory compliance.

A tiered approval process governs model deployment based on risk classification.

Credit and lending models are tested for disparate impact and fair lending compliance.

An AI-specific incident response plan addresses model failures, biased outputs, and data breaches.

Vendor AI solutions undergo due diligence for model risk, data handling, and regulatory compliance.

Comprehensive audit trails document model decisions, training data, and performance over time.

High-value AI use cases (fraud, risk, compliance) are ranked by business impact and feasibility.

A financial model tracks AI returns including cost savings, revenue uplift, and risk reduction.

AI strategy is presented and approved at board level with clear KPIs and milestones.

AI initiatives are designed with current and upcoming regulatory requirements in mind.

AI initiatives directly improve customer-facing metrics like onboarding time and NPS.

AI capabilities are positioned as competitive advantages in market positioning.

A dedicated AI innovation unit drives experimentation and scales successful pilots.

Strategic partnerships with fintechs, cloud providers, and AI vendors are established.