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Financial services: Resolving emissions liabilities end to end — Creating an agent that calculates portfolio emissions exposure and executes the required offset settlement workflow.

Financial services: Resolving emissions liabilities end to end

Advanced Analytica
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Creating an agent that calculates portfolio emissions exposure and executes the required offset settlement workflow.

ESG liabilities need an operating workflow, not another spreadsheet

This case study shows how a financial institution can move from emissions reporting to a governed emissions settlement workflow across portfolio holdings. The work connects exposure calculation, decision criteria, offset selection, approval, and settlement so liabilities do not remain trapped in disconnected spreadsheets and manual reconciliation steps. The agent gives the organisation a repeatable way to determine what action is required, link decisions to a defined framework, and execute the operational workflow once the relevant thresholds or obligations are met.

Challenge

  • Cloud and AI emissions liabilities were difficult to see across portfolio holdings.
  • Exposure calculations, offset decisions, and settlement actions sat in separate manual processes.
  • The institution needed a repeatable way to determine what action was required.
  • Manual reconciliation created delay between identifying a liability and settling it.

Approach

  • Defined the emissions calculation model for each holding and operating category.
  • Built an agent that calculates exposure, applies the offset framework, and determines the required action.
  • Connected the decision flow to a settlement process with a nature credit provider.
  • Added controls for auditability, thresholds, exceptions, and human review where required.
  • Created a clear record linking exposure, decision, and settlement action.

Outcome

  • Emissions liabilities are identified and settled through one continuous process.
  • Portfolio teams see exposure and required action in a single workflow.
  • Offset decisions are consistent because they follow a defined framework.
  • The institution reduces manual reconciliation while improving auditability.

Real-world example

An investment institution needed to see and act on cloud and AI emissions liabilities across its portfolio. We built an agent that calculates exposure, determines the offset action, and executes settlement with the provider.

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Find out where this case study can create value in your business.

This case study shows how agentic AI can turn knowledge and process into a controlled workflow. The useful next step is to identify where the same pattern applies inside your own processes, which controls need to be explicit, and what evidence is needed before agents move into production.

We turn that into a practical route from opportunity to governed agentic AI in production.

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Most businesses are sitting on knowledge they can't use at speed. The people who hold it are busy, the documents that contain it are static, and the processes built around it weren't designed for AI.

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