Client
Industry
Services
Project Duration
Customer Vision
Moody’s Analytics was expanding their Environmental, Social, and Governance (ESG) capabilities, with a specific focus on quantifying environmental and geopolitical risk across large, global company footprints. For enterprises with thousands of physical locations worldwide, Moody’s needed a scalable way to assess how environmental and external risk factors could impact credit risk and ratings.
We partnered with Moody’s Analytics to design and implement the data foundations and ESG data pipelines that powered their location-level risk assessments.
Our work focused on data ingestion, enrichment, orchestration, and delivery, enabling Moody’s to operationalize ESG risk scoring across thousands of global assets.
The Challenge
Moody’s faced several interconnected challenges:
· Highly fragmented data sources across corporate structures, facilities, financials, and external risk providers
· Complex corporate hierarchies, with parent companies, subsidiaries, and operating entities spanning multiple geographies
· The need to evaluate environmental and geopolitical risk at the individual location level, not just at the company level
· A requirement to deliver clean, structured, analyst-ready data that could be trusted in downstream credit and risk workflows
Critically, the risk scoring algorithms themselves were owned by a third-party provider, and Moody’s needed robust infrastructure to feed those models with accurate, complete, and timely data at scale.
Approach
The team at Roko Labs designed and implemented a robust, end-to-end data pipeline that unified, enriched, and operationalized ESG risk data across Moody’s global footprint. The solution began with integrating enterprise and location data to establish a clear and consistent mapping between companies and their physical assets. By connecting internal datasets and resolving corporate hierarchies, the system created a reliable foundation for location-level ESG analysis. Building on this foundation, external financial and market data was incorporated to provide additional context. This enrichment ensured that ESG risk signals could be analyzed alongside financial performance, strengthening the relevance of the insights for credit evaluation. A key component of the solution was the development of a scalable input pipeline for the ESG scoring API. This pipeline standardized and normalized data from multiple sources, preparing it for consumption by third-party models. It enabled Moody’s to incorporate a wide range of environmental and geopolitical risk factors, including climate exposure, extreme weather events, and regional instability. To support reliability and transparency, the entire data flow was orchestrated through a structured cloud architecture. Data was processed through layered stages - from raw ingestion to refined, analytics-ready datasets - ensuring consistency, traceability, and auditability at every step.
Key Outcomes
The new ESG data pipeline fundamentally changed how Moody’s approaches risk analysis. Analysts now have direct access to location-level ESG risk scores, allowing them to incorporate environmental and geopolitical factors into credit ratings and decision-making processes with far greater precision. What was once a high-level, assumption-driven exercise has become a data-driven, granular analysis grounded in real-world conditions. The platform also enables Moody’s to assess risk across thousands of global locations per company, providing a level of visibility that was previously difficult to achieve. This expanded perspective enhances both the depth and reliability of their insights. Operationally, the solution significantly reduced the need for manual data preparation. By automating data ingestion, enrichment, and downstream data processing, Moody’s improved consistency while accelerating the delivery of ESG risk insights to analysts. Perhaps most importantly, the new architecture established a scalable foundation for the future. Moody’s can now extend its ESG capabilities by incorporating additional models, data sources, and risk dimensions without reworking the core system.
Business Impact
By transforming a fragmented data ecosystem into a unified ESG intelligence platform, Moody’s Analytics strengthened its ability to deliver accurate, actionable risk insights at scale. The organization is now better equipped to evaluate how environmental and geopolitical factors influence credit risk, differentiate its ESG risk analytics offerings in the market, and support more informed decision-making for its clients. What began as a data engineering initiative has become a strategic capability, enabling Moody’s to operationalize location-based ESG analysis in a way that is both scalable and deeply integrated into its core workflows.
