Modules

Module 1:

The New Frontier – Climate Risk and Finance in Banking – Foundational Aspects

Focus:

Foundations and the unique nuances of climate risk within the financial ecosystem.
From Climate Risk to Financial Risk – “How Climate Risk Rewrites Traditional Risk”

Goal:

To understand the “Financialization” of climate concepts into banking risk pillars.
Universalization the language of climate risk: Across various functions of Banks be it Retail, Corporate, Risk, Compliance or Finance, an understanding how climate events hit the bank’s books.

Learning Objectives:

Distinguish between physical, transition, and liability risks through a banking lens.
Identify transmission channels that transform climate events into Credit and Market shocks.
The Transmission Mechanics: Flow of how a physical event (flood) or a transition event (carbon tax) cascade into Credit, Market, and Liquidity risk.

Core Content:

Risk Drivers: Physical, Transition, and Liability risks.
Financial Risk Translation : Transmission channels (how climate events become financial losses) and amplification mechanisms (feedback loops in the economy).
AI Augmentation: Using AI Models to ingest and summarize sprawling regulatory papers and climate science reports into actionable banking briefs.

Learning Outcomes

Participants will:

Differentiate physical vs transition vs liability risks
Explain how climate risk enters banking systems
Identify sectoral exposure patterns
Understand role of AI-enabled insights in Interpreting climate risk signals.

Value:

Participants can connect climate risk to their day jobs (finance, lending, risk, treasury, etc.)

Module 2:

The Data Engine – Climate Information Architecture and the AI edge

Focus:

Addressing the “data gap” – the primary hurdle in climate finance.
Moving from Data Scarcity to Decision Intelligence

Goal:

Getting to the Core – Climate Information Architecture & AI
External vs. Internal vs. Proxy data play

Learning Objectives:

- Drawing up data maps of key data sources
- Data standardization, Normalization and Integration
- PCAF standards and Data Quality score

Core Content:

Sourcing structured and unstructured climate data; PCAF standards; dealing with data gaps and quality issues.
The Map: of different data sources to find and use data when traditional financial statements are silent on climate
AI Augmentation, The AI Edge: The role of AI in data cleaning, proxy data generation, and satellite imagery analysis for asset-level insights.

Learning Outcomes

Participants will:

Appreciate the centrality of flexible Climate Information Architecture
Know climate data ecosystems
Address data gaps using proxies
Apply AI-enabled data extraction and enrichment

Value:

Participants learn how banks map and grow climate data structures organically.

Module 3:

The “Quants”: Climate Risk Assessment, Measurement & Stress Testing

Focus:

Integrating climate into the existing Basel framework and risk pillars.
Quantifying the Uncertain

Goal:

Sizing the impact. How climate impacts the traditional “Pillars” of Banking Risk.

Quantify the impact on the bank’s capital and benchmark it against bank’s risk appetite

Learning Objectives:

Developing Climate Risk Appetite Statement (RAS) that aligns with business strategy.

Incorporating climate variables into risk identification and assessments
Execute Scenario Analysis ( NGFS pathways -Network for Greening the Financial System) pathways and gauge the impact of “Stranded Assets” on the books.

Core Content:

Defining climate risk appetite of the bank
Identifying, assessing, measuring risks and KPIs
Scenario Analysis: Stress testing under various NGFS (Network for Greening the Financial System) pathways. Identifying portfolio vulnerabilities.

AI Augmentation: Proactive identification of borrower/portfolio vulnerabilities, identifying “Transition-Laggards” before they default

Learning Outcomes

Participants will:

Draft a risk appetite statement
Apply scenario analysis frameworks
Understand portfolio sensitivity
Use AI to enhance vulnerability modelling

Value:

Participants learn how banks actually build usable climate intelligence, not just theory.

Module 4:

Integrity Reporting: Climate Disclosure & Regulatory Integration

Focus:

Navigating the global regulatory landscape and stakeholder expectations.
Translate climate risk into regulatory-grade disclosures and governance structures.

Learning Objectives:

Navigate the requirements of TCFD, ISSB (S1 & S2), and Pillar 3 disclosures.
Manage “Double Materiality” how the bank impacts the climate and vice versa.
Managing Reputational and Legal/Liability risks (Greenwashing prevention).

Core Content:

- Reporting Landscape and TCFD Disclosure Architecture, Understanding what regulators want to see in the annual report.
- Global Standards and important regional nuances: ISSB (s1, s2), ESRS…
- Pillar 3 Disclosures: Integrating climate into the bank’s public risk disclosures.

AI Augmentation: Maintaining integrity across reports both external reporting as also between external posture and internal realty eg -Cross-references draft public disclosures against the bank’s internal climate data to flag any “Over-Claiming” or inconsistencies before they are published.

Learning Outcomes

Participants will:

Understand global disclosure standards
Align internal reporting with regulatory expectations
Design governance frameworks
Use AI for compliance efficiency

Value:

Roadmap to move from Compliance to Credibility

Module 5:

Strategic Integration– Creating an Effective Operating Model for Balance Sheet & Business Transformation

Focus:

Moving climate risk from a “silo” to the boardroom and Balance Sheet.
To embed climate risk insights into the bank’s governance and strategy.

Learning Objectives

Define roles across the “Three Lines of Défense.”
Relationship, connectivity between different components of Operating Model
Designing of Governance frameworks

Core Content:

Blueprint for Strategic, Structural and Operating Frameworks.
Governance & The 3 Lines of Defence: Who is responsible for climate risk from the teller to the Board.
AI Augmentation: A real-time dashboard that tracks lending activities against its “Green Targets,” providing an “Early Warning System”, Green financing, transition finance opportunities etc

Learning Outcomes

Participants will be able to

Draw up a strong Operating Model.
Strengthen communication and clarity of responsibilities and accountability across roles.
Integrate climate risk into ERM frameworks

Value:

Moving from risk awareness to strategic decision-making, From Risk management to Competitive Advantage

Module 6:

Capstone Project – Bringing it all together

**Projects will be assigned during week one, so participants get sufficient time to work through the project.

Focus:

To synthesize all learning into a professional-grade strategic artifact.
Climate Risk Transformation Blueprint for a Bank

Objectives:

Practical application of the connected continuum of modules.
Integrate climate risk into the standard credit underwriting process for a specific corporate borrower.

Core Content:

Designed to be application artifacts. Bridge the gap between high-level climate theory and the day-to-day mechanics of banking operations
Provide a tangible deliverable that participants can present to their internal stakeholders (Risk Committees, Board, or Department Heads) to demonstrate application and ROI.
AI Augmentation: Use cases at any and every step of the project – participants to identify

Learning Outcomes: