CIB AI Strategy

  • Executive Summary
  • Business Outcomes
  • Use-Case Portfolio
  • Target Architecture
  • GenAI & RAG Patterns
  • MLOps & Controls
  • Data Governance
  • Operating Model
  • Delivery Roadmap
  • KPIs & Economics
  • Risk Register
  • Appendices

Executive Summary

An actionable, compliant-by-design AI strategy to drive measurable value across Corporate & Institutional Banking (CIB) on the Azure and Databricks platform.

Target 12-Mo ROI

1.8x - 2.5x

Across prioritized use cases

Prioritized Use Cases

10+

Covering Risk, Markets, & Ops

Time to First Value

< 90 Days

Via RAG Copilot & Analytics POC

Compliance Alignment

100%

Model Risk, BCBS 239, Privacy

Strategic Vision

This strategy establishes a robust foundation for AI innovation within CIB, transforming our core business lines through intelligent automation, advanced analytics, and generative AI capabilities. By leveraging our strategic investment in Azure and Databricks, we will enhance client outcomes, optimize risk management, and drive operational efficiency. The approach is grounded in a compliant-by-design framework, ensuring that all initiatives meet stringent regulatory and model risk management standards from inception.

Key Recommendations:

  • Immediately fund and launch the Top 3 prioritized use cases: RFQ Win Propensity, Trade Finance Document Intelligence, and Corporate Credit Early Warning Signals.
  • Formalize the CIB AI Center of Excellence (CoE) to centralize governance, platform engineering, and skills development.
  • Implement the proposed Target Architecture, standardizing on Unity Catalog for governance and MLflow for MLOps.
  • Execute the 30/60/90 Day Plan to build momentum and demonstrate tangible value quickly.

Business Outcomes & North Star

Our AI strategy is anchored to three primary value pillars, ensuring every initiative delivers measurable impact for our clients, shareholders, and regulators.

Enhance Revenue & Client Value

Deepen client relationships and increase market share through data-driven insights and personalized services.

  • Increase RFQ win-propensity
  • Identify cross-sell opportunities
  • Optimize pricing and execution
  • Deliver personalized cash-flow forecasting

Strengthen Risk & Compliance

Proactively manage risk, automate controls, and exceed regulatory expectations with transparent and auditable AI.

  • Improve credit risk early-warning signals
  • Reduce AML false positives
  • Automate trade-based money laundering detection
  • Ensure data lineage for risk reporting (BCBS 239)

Drive Operational Efficiency

Automate manual processes, reduce operational risk, and empower our employees with AI-powered copilots.

  • Automate document intelligence for Trade Finance
  • Accelerate client onboarding and KYC
  • Reduce trade breaks and reconciliation time
  • Provide AI assistants for operations and coverage

Use-Case Portfolio

This section provides an interactive view of the prioritized AI use cases. We've scored each initiative based on business value, technical feasibility, and regulatory alignment to create a balanced, high-impact portfolio.

Portfolio Overview: Value vs. Feasibility

Prioritized Use-Case Details

Rank Use Case Business Outcome Build vs Buy Size

Target Data & AI Architecture

Our target architecture is built on Azure and Databricks, providing a unified, governed, and scalable platform for all AI and analytics workloads. The design emphasizes security, compliance, and operational excellence through a Medallion architecture, centralized governance with Unity Catalog, and robust MLOps with MLflow.

Azure Cloud & Security Foundation (VNet, Private Link, Key Vault, AAD)

Sources

Core Banking, Risk, Market Data, CRM, Payments
→

Ingestion

Event Hubs (Streaming), ADF (Batch), Auto Loader
Databricks Lakehouse Platform
Bronze (Raw)
Silver (Cleansed)
Gold (Aggregated)
Delta Lake & Medallion Architecture

Unity Catalog

Governance, Lineage, Access Control

MLOps (MLflow)

Registry, Serving, Monitoring

AI/ML

Feature Store, Model Training, Azure OpenAI

BI & SQL

Databricks SQL, Power BI

GenAI / RAG

Vector Search, Agents
Business Consumers & Applications

GenAI & RAG Patterns for CIB

We will leverage Generative AI safely and effectively using a standard Retrieval-Augmented Generation (RAG) pattern. This ensures that all GenAI applications are grounded in our own governed, high-quality data, minimizing hallucinations and providing auditable, attributable responses.

Core RAG Blueprint

  1. Governed Data Sources: CIB documents (deal memos, policies, KYC files) are curated in the Databricks Lakehouse and governed by Unity Catalog.
  2. Chunking & Embedding: Documents are split into manageable chunks and converted into numerical representations (embeddings) using a secure model.
  3. Vector Database: Embeddings are stored in Databricks Vector Search, indexed for fast and secure similarity search.
  4. User Prompt: A user asks a question via a copilot application (e.g., "Summarize the covenants for Client X").
  5. Retrieval: The system finds the most relevant document chunks from the Vector DB based on the user's prompt.
  6. Augmentation & Generation: The original prompt and the retrieved chunks are sent to a secure Azure OpenAI model (e.g., GPT-4) with a system instruction to answer *only* based on the provided context.
  7. Response & Citation: The model generates a response and the application provides citations back to the source documents, ensuring transparency and auditability.

High-Priority RAG Use Cases

Trade Finance Document Intelligence

Automate extraction and validation of data from Letters of Credit (LCs), Bills of Lading (BLs), and certificates to reduce manual effort and errors.

RFQ Sales Copilot

Provide relationship managers with real-time talking points, past trade history, and client insights when responding to a Request for Quote (RFQ).

KYC Refresh Assistant

Accelerate periodic Know-Your-Customer (KYC) reviews by summarizing internal documents and flagging changes against policy requirements.

Covenant & Credit Agreement Analysis

Enable credit officers to quickly query and compare terms across complex credit agreements and legal documents.

MLOps, Model Risk, and Responsible AI

Our MLOps framework operationalizes model risk management and Responsible AI principles, ensuring every model is developed, deployed, and monitored in a secure, transparent, and compliant manner.

Standardized MLOps Lifecycle with MLflow

  • CI/CD Integration: All code (notebooks, libraries) and infrastructure (Terraform) is managed through Azure DevOps / GitHub Actions for automated testing and deployment.
  • MLflow Model Registry: A central repository for all models, with versioning, stage transitions (Dev, Staging, Prod), and documented approvals. Each model has a "model card" detailing its purpose, data, and limitations.
  • Automated Deployment: Approved models are automatically deployed to Databricks Model Serving for real-time endpoints or as batch inference jobs.
  • Comprehensive Monitoring: Continuous monitoring for model drift, performance degradation, and data quality issues. Champion-challenger frameworks are used for safe rollout of new models.

Integrating with the Three Lines of Defense

  • 1st Line (Model Owners/Developers): Responsible for comprehensive documentation, development evidence, and ongoing monitoring, logged via MLflow and Confluence.
  • 2nd Line (Model Risk Management): Independent validation of model methodology, performance, and limitations before first-time use. Reviews are triggered by stage transitions in the MLflow Registry.
  • 3rd Line (Internal Audit): Periodic review of the end-to-end process, controls, and governance to ensure compliance with bank policy.
  • Human-in-the-Loop (HITL): High-impact decisions (e.g., large credit approvals) require human review. All overrides and justifications are captured for audit.

Principles in Practice

  • Fairness & Bias Detection: Models are tested for bias against sensitive features during development and monitored in production. Mitigation strategies are applied where necessary.
  • Transparency & Explainability: Use of tools like SHAP to explain model predictions, especially for risk and client-facing use cases. GenAI responses must provide source attribution.
  • Privacy & Data Minimization: Adherence to the principle of least privilege. PII is tokenized or masked where possible. GenAI prompts are scrubbed of sensitive data before being sent to models.
  • Security & Safety: GenAI applications include prompt safety guardrails, content filters, and defenses against jailbreaking. All prompts and outputs are logged for audit.

Data Governance & Quality (BCBS 239-aligned)

Our data governance framework is the bedrock of trustworthy AI. By implementing robust controls aligned with BCBS 239 principles, we ensure all data used for analytics and model development is accurate, complete, timely, and fully traceable, building confidence with stakeholders and regulators.

Core Governance Principles via Unity Catalog

Unity Catalog serves as the single pane of glass for governing all our data and AI assets on the Databricks Lakehouse.

  • ✓

    Unified Governance

    Centralized control over all data assets, including files, tables, dashboards, and machine learning models.

  • ✓

    Fine-Grained Access Control

    Secure access using Azure AD identities with standard SQL grants for row, column, and attribute-based permissions.

  • ✓

    Automated Data Lineage

    End-to-end, column-level lineage graphs tracking data from source to consumption, critical for risk reporting and impact analysis.

Data Quality Framework

We enforce data quality proactively throughout the data lifecycle, ensuring issues are caught and remediated at the source.

  • ✓

    Quality Gates in Pipelines

    Using Delta Live Tables with "Expectations" to define and enforce quality rules (e.g., null checks, valid values) as data moves from Bronze to Silver to Gold.

  • ✓

    Data SLAs & SLOs

    Formal Service Level Agreements (freshness, completeness) are defined for critical Gold datasets and monitored continuously.

  • ✓

    Quality Monitoring Dashboards

    Power BI dashboards surface quality scores and SLA adherence to data owners, promoting accountability and transparency.

Target Operating Model & AI CoE

Our operating model is designed to foster innovation while maintaining strong governance and control. It balances centralized expertise within an AI Center of Excellence (CoE) with business-aligned execution squads to accelerate value delivery.

Hub-and-Spoke Organizational Model

A central CIB AI CoE (the hub) provides the platform, governance, and specialized expertise. Business-aligned squads (the spokes) own and deliver specific use cases, ensuring tight alignment with business priorities.

AI CoE Core Pods:

  • Platform Engineering: Manages the core Azure + Databricks platform, MLOps tooling, and GenAI infrastructure.
  • Governance & Controls: Acts as the bridge to Model Risk Management and Compliance, sets standards for Responsible AI, and manages AI-related policies.
  • Enablement & Best Practices: Develops training programs, curates reusable assets ("golden patterns"), and fosters an internal AI community.

RACI for Model Lifecycle:

  • Responsible (1LOD): Business-aligned "Model Owner" and "Model Developer" squads.
  • Accountable (1LOD): Head of the relevant business line or function.
  • Consulted (2LOD/3LOD): Model Risk Management, Compliance, Internal Audit.
  • Informed: Platform CoE, Senior Management.

Building a Data-Driven Culture

We will invest in upskilling our talent to ensure they can effectively leverage and contribute to our AI initiatives.

  • Role-Based Learning Paths: Customized training for key personas:
    • Bankers & Traders: Focus on using AI tools, interpreting model outputs, and identifying new opportunities.
    • Risk & Compliance Analysts: Training on model validation principles, bias detection, and AI governance.
    • Quants & Data Scientists: Advanced training on MLOps, GenAI, and the Databricks platform.
  • Secure Sandboxes for Innovation: Governed Databricks workspaces to allow for safe experimentation with new techniques and data sources.
  • "Golden Patterns" & Reusable Assets: A library of pre-built code templates and project accelerators for common tasks (e.g., RAG boilerplate, model deployment pipelines) to improve speed and consistency.

Strategic Build-vs-Buy Framework

Each initiative is assessed through a strategic lens to determine the optimal sourcing strategy.

Decision Rubric Criteria:

CriteriaConsiderations
Strategic IPDoes this capability provide a sustainable competitive advantage? (Favors Build)
Time to MarketHow quickly can we realize value? (Can favor Buy)
Compliance & IntegrationHow complex is it to integrate with our systems and meet regulatory requirements? (Can favor Build)
Total Cost of OwnershipIncludes licensing, implementation, and ongoing maintenance vs. internal development and support costs.

Default Stance:

  • Build: Core risk (PD/LGD), pricing, and client-facing models where IP and deep integration are critical.
  • Build on Platform: All Generative AI / RAG use cases to ensure data privacy and control.
  • Buy & Integrate: Commoditized capabilities or areas where vendors have a significant R&D advantage (e.g., specific AML typologies, OCR engines).

Delivery Roadmap

A phased roadmap to build foundational capabilities, deliver quick wins, and scale value across the CIB portfolio over the next 24 months.

First 30 Days: Foundation & Quick Wins

Focus on establishing core governance, launching initial POCs, and building momentum.

  • Stand up governed RAG pilot on KYC procedures corpus.
  • Develop baseline model for RFQ Win Propensity analytics use case.
  • Finalize CIB AI CoE charter and key roles.
  • Establish Unity Catalog governance for top 3 use case data sources.

Next 60 Days: Pilots & Platform Hardening

Expand pilots, solidify MLOps foundations, and demonstrate initial value to stakeholders.

  • Launch RFQ Sales Copilot pilot with a small group of traders.
  • Complete POC for corporate cash-flow forecasting.
  • Implement MLflow Registry with stage gates and CI/CD pipeline for model deployment.
  • Define data quality SLAs/SLOs for Silver-level tables.

Next 90 Days: Production & Value Realization

Move the first models to production, publish the benefits tracker, and secure the full-year budget.

  • Deploy RFQ Win Propensity model into production with live monitoring.
  • Go-live with Trade Finance Document Intelligence RAG for one document type.
  • Publish first iteration of the AI Benefits Tracker dashboard in Power BI.
  • Present 90-day outcomes and secure formal FY budget approval.

12-24 Months: Scale & Institutionalize

Expand the portfolio, deepen capabilities, and embed AI as a core competency within CIB.

  • Scale development to the Top 10 prioritized use cases.
  • Build out a shared Feature Store to accelerate model development.
  • Establish a unified observability dashboard for all production models.
  • Institutionalize the model risk lifecycle within the MLOps framework.
  • Expand automated narrative generation for management reporting packs.

KPIs, OKRs, & Economics

This section outlines how we will measure success. Our framework connects high-level Key Performance Indicators (KPIs) to specific Objectives and Key Results (OKRs) and provides a pragmatic economic model for investment planning.

Core KPI Dashboard

We will track a balanced scorecard of metrics across our key value pillars. Targets will be set against established baselines in the first 90 days.

OKR Framework Example

Each initiative will be driven by OKRs to ensure focus on measurable outcomes. OKRs will be reviewed quarterly.

Objective: Increase RFQ Market Share through AI-driven Pricing Support.

  • Key Result 1:Increase RFQ hit-rate for medium-sized trades by 15 bps.
  • Key Result 2:Reduce trader response time for targeted RFQs by 20%.
  • Key Result 3:Achieve a model user satisfaction score of >8/10 from the pilot trading desk.

Budget & Economics (T-Shirt Sizing)

Size Est. Cost (12 mo) Expected ROI Window Cloud Cost Levers
Small (S) $150k - $300k 6-9 months Job Compute, Spot Instances, Photon Engine
Medium (M) $300k - $750k 9-15 months Above + Cluster Policies, Liquid Clustering
Large (L) $750k - $1.5M+ 12-24 months Above + Reserved Instances, FinOps Dashboards

Risk Register

A register of key risks associated with the AI strategy, along with their mitigations and owners. This is a living document that will be reviewed and updated quarterly by the AI CoE.

Risk ID Description Category Impact/Like. Mitigation / Control Owner
AI-001 Sensitive data leakage via GenAI prompts or RAG sources. Data High/Med Mandatory RAG on governed data; PII scrubbing; logging prompts and outputs; no raw data in prompts. CISO / AI CoE
AI-002 Unapproved model changes bypassing MLOps controls. Compliance High/Low Mandatory CI/CD via Azure DevOps; MLflow Registry stage-gate approvals; branch protection rules. Head of MLOps
AI-003 Cloud cost overrun due to uncontrolled interactive compute. Change Med/High Databricks cluster policies; budget alerts; FinOps dashboard monitoring; favor Job Compute. Platform Owner
AI-004 Model performance degrades silently in production, leading to poor business decisions. Model Med/Med Continuous monitoring for data/concept drift; automated alerts; champion-challenger deployment. Model Owner
AI-005 Vendor model deprecation or breaking API changes disrupt service. Security Low/Med Use Azure OpenAI as managed layer; abstraction layer in code; periodic review of vendor roadmaps. AI CoE

Appendices

Supporting documentation including a glossary of terms, key assumptions made in this strategy, and a log of foundational decisions.

BCBS 239: Basel Committee on Banking Supervision's principles for effective risk data aggregation and risk reporting.
MLOps: (Machine Learning Operations) A set of practices that aims to deploy and maintain machine learning models in production reliably and efficiently.
RAG: (Retrieval-Augmented Generation) An AI framework that retrieves facts from an external knowledge base to ground large language models (LLMs) on the most accurate, up-to-date information and to give users insight into the LLMs' generative process.
Unity Catalog: A fine-grained governance solution for data and AI on the Databricks Lakehouse.
  • Strategic commitment to the Azure + Databricks platform is maintained for the next 24-36 months.
  • Required funding for the AI CoE and prioritized use cases will be approved as outlined in the budget.
  • Timely access to required data sources will be granted, subject to standard data governance approvals.
  • The bank can attract and retain the necessary talent (Data Scientists, ML Engineers) to execute this strategy.
  • The current regulatory landscape for AI in financial services remains broadly consistent, without prohibitive new restrictions.
DecisionRationale
Standardize on Unity Catalog for all data and AI access control.Provides a single, unified governance model across all workspaces and assets, simplifying security and audit.
All GenAI solutions must use an internal RAG pattern.Ensures data privacy, minimizes hallucinations, and provides auditable, attributable responses grounded in CIB's own data.
CI/CD is mandatory for all production AI/ML workloads.Enforces robust testing, version control, and approval gates, satisfying model risk management requirements.
Prioritize "Build" for models creating strategic IP.Maintains competitive differentiation in core areas like risk, pricing, and client analytics.

© 2025 Bayesian AI Solutions Consulting Partners

0