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Turning manual reporting risk into AI-powered investment insight
Introduction
We supported a major asset manager in replacing a manual, risk-prone regulatory reporting process with an AI-powered platform. We led the full vendor selection, requirements design, and target operating model development.
The challenge
The organisation's annual regulatory reporting of carbon emissions across its private credit borrowers was highly manual and operationally risky. The process was slow, inconsistent, and produced limited data insight. This made it difficult for the business to accurately assess carbon exposure across current deals and those in the pipeline. The absence of a scalable, reliable reporting mechanism represented both a compliance risk and a strategic blind spot at the point of investment decision-making.
The solution
We managed the end-to-end vendor selection process, assessing multiple third-party SaaS platforms. We advised business stakeholders on the strengths and limitations of each option to support an informed decision. Once a platform was selected, we authored detailed requirements and developed a Target Operating Model for the new solution. The selected platform was designed to automate the reporting workflow and leverage LLM-based AI to achieve greater completeness and accuracy of data.
The benefit delivered
With implementation underway, the organisation will gain dashboard-based visibility of carbon emissions across both existing and pipeline deals for the first time. This will enable more confident, data-driven investment decisions. It will also substantially reduce the operational risk associated with manual processing and create a scalable foundation for regulatory reporting going forward.