Level 2–3 · Specialization · 4 to 6 days

Banking
Governance

From regulatory compliance to the Data & AI Master Plan

6 modules anchored in the specific challenges of the financial sector. DORA, BCBS 239, EU AI Act, AI agent governance in banking and CDO leadership — the complete learning path for financial sector decision-makers.

M1 · Governance & Compliance M2 · Importance of Governance M5 · Responsible GenAI M6 · AI Ethics & Decision M7 · Banking Agentic AI M8 · Master Plan & TOM
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This learning path at a glance
6
Modules
4–6d
Duration
C-Level
Target Audience
Lvl.3
Level
M1 · Governance & Compliance1d
M2 · Importance of Governance½d
M5 · Responsible GenAI½d
M6 · AI Ethics & Decision½d
M7 · Banking Agentic AI1d
M8 · Master Plan & TOM1d
Learning Sequence · 6 steps

From regulatory framework to CDO leadership

M1–M2 lay the compliance and data governance foundations. M5–M6 address responsible AI and ethical decision-making. M7 dives into banking agentic AI. M8 synthesizes into a Master Plan.

M1
Step 1 · Compliance foundationsRisk · Compliance · CIODORA · BCBS 239 · GDPR
Data Governance & Compliance
Data as a strategic asset, the 2026 regulatory framework, key roles and the organization's AI policy.
1 day▾
Program
  • Data as a strategic asset in banking
  • Strategy & Data Governance Framework
  • Key data governance roles
  • Data management processes
  • Data security — The CIA model
  • The 2026 digital regulatory framework (DORA, BCBS 239, GDPR)
  • AI Governance — The organization's AI policy
  • Operational compliance plan
Deliverables
  • 📋
    2026 Regulatory Matrix
    DORA · BCBS 239 · GDPR · EU AI Act — concrete obligations by regulation
  • 🛡️
    Organization's AI Policy
    Structure and content of a compliant AI policy — adaptable template
  • 🗓️
    Compliance Plan
    Operational roadmap — milestones, owners, success criteria
📖
Self-Learning Module Available
Self Learning — Data Governance & Compliance
→
↓
compliance → strategic governance challenges
M2
Step 2 · Strategic challengesExecutive Committee · CDO · Risk
The Critical Importance of Data Governance
Silent risk, Data Lineage, generative and agentic AI — why data governance has become business-critical.
½ day▾
Program
  • Poorly governed data — the silent risk that becomes systemic
  • Data Lineage — definition, challenges and regulatory obligations
  • Data Lineage in practice — what happens without it
  • Generative AI changes everything — why data becomes even more critical
  • Agentic AI — when autonomous agents act on your data
  • The most common failures — what actually happens
Deliverables
  • ⚠️
    Systemic Data Risk Radar
    10 real risks — probability, impact, and corrective measures
  • 🔗
    Data Lineage Diagram
    Data flows from source to reporting — annotated model for audit
  • 🤖
    Data x AI Agents Checklist
    What changes when autonomous agents act on your sensitive data
📖
Self-Learning Module Available
Self Learning — The Critical Importance of Data Governance
→
↓
governance → responsible AI use cases
M5
Step 3 · Responsible AI use casesAll employeesEU AI Act
Responsible GenAI in Banking
CARE method, 5 critical risks in a banking context, AI policy and ROI use cases by department.
½ day▾
Program
  • Shadow AI in banking — what is already happening in your organization
  • The CARE method — building a prompt that works
  • The 5 critical GenAI risks in a banking context
  • The responsible AI policy — organizational and employee obligations
  • GenAI in banking — high-ROI use cases by business department
Deliverables
  • 🎯
    Banking CARE Prompt Template
    Method adapted to the regulatory context — 5 examples per department
  • 🔴
    5 GenAI Risks Matrix
    Confidentiality, hallucination, bias, Shadow AI, EU AI Act non-compliance
  • 📊
    Banking ROI Use Case Map
    By department — Compliance, Risk, Retail, Wholesale, HR, Finance
📖
Self-Learning Module Available
Self Learning — Responsible GenAI in Banking
→
↓
use cases → ethics and augmented decisions
M6
Step 4 · Operational ethicsDecision-Makers · ManagersEU AI Act Art.13-14
AI Ethics & Augmented Decision-Making
Algorithmic bias, right to explanation, augmented decision-making and the 8-question ethical grid.
½ day▾
Program
  • AI ethics is not philosophical — it is operational
  • Algorithmic bias — how it forms, how to detect it
  • The right to explanation — what the EU AI Act concretely requires
  • Augmented decision-making — where AI helps, where humans must decide alone
  • The ethical decision grid — 8 questions before deploying
  • Banking ethical dilemmas — there is not always a right answer
Deliverables
  • ⚖️
    AI Ethical Decision Grid
    8 systematic questions before using or deploying an AI system
  • 📝
    EU AI Act Explainability Checklist
    Art.13-14 obligations for high-risk systems — immediately applicable
  • 🧩
    Human/AI Decision Matrix
    Decision mapping — what can be delegated, what must be retained
📖
Self-Learning Module Available
Self Learning — AI Ethics & Augmented Decision-Making
→
↓
ethics → banking agentic architecture
M7
Step 5 · Banking agentic AICDO · CIO · ArchitectsEU AI Act
Agentic AI — Banking Applications
7-layer architecture, 3 orchestration patterns, 4 critical guardrails and Banking Tech Radar.
1 day▾
Program
Architecture & governance
  • What an AI agent really is — beyond the chatbot
  • Architecture of an agentic system — the 7 layers to master
  • The 3 orchestration patterns — how agents work together
Controlled deployment
  • The 4 critical guardrails — what separates scalable from risky
  • Banking Tech Radar — Anchor · Activate · Secure · Scale
  • Hands-on exercise — qualifying an agentic use case and setting up its governance
Deliverables
  • 🏗️
    Agentic Architecture Diagram
    The 7 documented layers — LLM, memory, tools, orchestration, guardrails, monitoring, audit
  • 🔒
    4 Guardrails Grid
    Agent Policy, HITL, quotas, circuit breaker — conditions for industrialization in banking
  • 📡
    Banking Agentic Tech Radar
    4-phase roadmap — Anchor / Activate / Secure / Scale
📖
Self-Learning Module Available
Self Learning — Agentic AI — Banking Applications
→
↓
agentic AI → CDO leadership & Master Plan
M8
Step 6 · Strategic leadershipCDO · CIO · Executive Committee
Data & AI Master Plan — TOM & Leadership
Maturity, target TOM, 3-year roadmap, ROI business case and CDO dashboard.
1 day▾
Program
  • The maturity model — where do you stand across the 5 levels?
  • The Target Operating Model — the target Data & AI organization
  • The 3-year roadmap — from identification to scaling
  • The ROI business case — justifying the investment to the Executive Committee
  • Leadership — KPIs and CDO dashboard
  • Workshop — building the foundations of your Master Plan
Deliverables
  • 🎯
    Self-Assessed Maturity Model
    5 levels x 6 dimensions — positioning and progression plan
  • 🗺️
    Data & AI TOM Blueprint
    Target organization — roles, teams, interfaces and operating modes
  • 📊
    Executive Committee ROI Business Case
    Structure and figures — justification of Data & AI investment
  • 📈
    CDO Dashboard
    Leadership KPIs — data quality, AI adoption, compliance, program ROI
📖
Self-Learning Module Available
Self Learning — Data & AI Master Plan — TOM & Leadership
→
M1Compliance
→
M2Importance
→
M5GenAI
→
M6AI Ethics
→
M7Agentic AI
→
M8Master Plan
4–6 days
Total Duration
Banking & Insurance · In-Company or Inter-Company
Train your teams on Data & AI challenges in the financial sector
M1–M2 can be delivered as a standalone awareness foundation. M5–M8 constitute the advanced specialization for Data, Risk and IT teams.
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