Building an in-house climate scenario modeling capability for a global bank
Targeted coaching, challenger analysis, and tight governance helped a UK bank build its own climate scenario model in one quarter.
Starting point
A UK-based global bank had already used an external climate scenario model to understand its financed emissions and portfolio alignment with climate pathways. That experience confirmed two things. First, climate analytics needed to be rigorous enough to withstand scrutiny from internal validation teams, regulators, and external stakeholders. Second, over the long term the bank wanted its own engine, with internal ownership of methods, data, and governance rather than relying solely on vendors.
The brief was clear. The bank’s risk and finance leaders wanted an in-house climate scenario model that could quantify financed emissions, temperature alignment, and sector coverage across priority portfolios. The model needed to be technically credible, auditable, and aligned with the bank’s climate policies. It also needed to pass review by the Independent Validation Unit, which treats models as critical risk infrastructure.
What they lacked was an internal blueprint. The team needed to make a set of early design choices about asset versus issuer granularity, scope boundaries, climate pathway selection, and how to benchmark the realism of counterparties’ climate targets. They also wanted challenger analytics that could test and refine their build, rather than developing in isolation.
Approach
The bank needed a core advisor and engaged an external consulting team that included Bhuvan Maingi, who is now leading Strathen Group. The mandate was to stand up a usable climate modeling capability in under one quarter, while transferring skills and ensuring the build would stand up to independent validation.
The work began with model design choices. The team worked with risk, finance, and modeling leads to define the overall architecture: an asset-to-issuer-to-portfolio paradigm with configurable Scope 1, 2, and 3 coverage. Together they agreed where asset-level detail was essential, where issuer-level approximations were acceptable, and how granular the model needed to be for priority sectors such as oil and gas and power utilities.
Next came data strategy. The advisors defined a data foundation that drew on Bloomberg and Trucost for historical emissions and company fundamentals, with clear rules for data lineage, quality checks, and refresh cadence. They documented how to handle low-disclosure counterparties, how to reconcile holdings data with coverage in the climate model, and how to manage versioning so that results could be reproduced and audited.
In parallel, the team helped configure a scenario library. This included 1.0, 1.5, and 2.0 degree temperature pathways, scope toggles to isolate Scope 1, 2, and 3 contributions, and parameter choices aligned to the bank’s climate policies and disclosure cadence. The team agreed how to express outputs such as carbon intensity and temperature alignment in ways that investment committees, risk committees, and boards could understand and act on.
A central part of the engagement was using an established climate model as a challenger. The external Climate Change Scenario Model was configured as a comparison engine, running across the bank’s counterparties in oil and gas and power utilities. For each of three scenario runs covering up to 200 counterparties, including up to 50 unlisted names, the team compared outputs from the bank’s in-house model against the challenger. Variances were traced back to data assumptions, sector mappings, or methodological choices.
These challenger-model comparisons served two purposes. They highlighted where refinements were needed in the bank’s build and gave stakeholders confidence that the internal engine could separate issuers within a sector based on genuine differences in emissions trajectories, risk profiles, and target realism, not just sector averages.
Alongside the technical work, the advisors engaged closely with the bank’s Independent Validation Unit. Together they defined what evidence IVU would need to sign off on the model: documentation of data sources and transformations, scenario assumptions, sensitivity tests, and explanations of any model limitations. QA checklists, run books, and validation logs were created so that future runs could be executed and reviewed consistently.
The engagement also looked ahead to sustainability. The team supported role design and hiring for the internal function, clarifying the skills mix needed across modeling, data engineering, risk reporting, and climate expertise. The goal was to ensure that the bank could operate and evolve the model itself, rather than treating the build as a one-off project.
Instead of importing a black-box model, the bank built its own climate engine with independent validation in mind from day one.
Outcome
Within a single quarter, the bank moved from aspiration to an operational climate scenario modeling capability. The internal engine could quantify financed emissions and temperature alignment across priority portfolios in oil and gas and power utilities, with configurable scopes and temperature pathways.
Risk and finance teams now had decision-ready outputs: issuer-level carbon intensity and temperature alignment, portfolio heatmaps showing concentration by sector and risk driver, and scenario comparisons that could be used in capital allocation and risk discussions. Because underlying data lineage and assumptions were documented, those outputs could be defended in front of internal committees, regulators, and external stakeholders.
The challenger-model runs provided an extra layer of assurance. Where the internal model diverged from the external benchmark, the team could explain why, refine logic, or adjust data mappings. This process improved the robustness of the bank’s engine and gave the Independent Validation Unit a clear trail from input to output.
From a capability perspective, the bank ended the engagement with more than a model. It had a playbook for running scenarios, QA checklists embedded in business as usual, and an internal team equipped to maintain and enhance the engine as climate policy, disclosure standards, and portfolio composition evolve.
For risk and finance leaders, the real shift was treating climate analytics as a core model under governance, not a one-off consultancy slide.
This work continues to shape how Strathen Group helps financial institutions build climate capabilities today, focusing on independent ownership, transparent methods, and challenger analytics that turn climate risk from a reporting obligation into a managed decision variable.
