Decision-grade intelligence in an AI-driven world

AI has changed the speed of decisions, but not the cost of getting them wrong

The world is entering an era of exponential risk — where risks don't stay in silos. They interact, multiply, and compound with unprecedented speed. Credit shocks, geopolitical disruptions, extreme weather events, supply chain failures, and sanctions exposure no longer unfold in sequence — they collide across domains simultaneously.

AI has changed the speed of decisions. It has not changed the cost of getting them wrong. The institutions that will lead in an AI-driven world are not those that move fastest — they are those that ground AI in intelligence that can actually be trusted, defended, and acted upon.
 

The problem is not data. It’s trust

AI has transformed the economics of information. AI tools can now synthesize research, automate workflows, and accelerate decisions that once took weeks. The speed has surfaced a harder question, however: which outputs can actually be acted upon?

For regulated institutions, the bar is not whether AI can generate an answer. It is whether that answer is valid, explainable, auditable, and built on data their customers, regulators and boards of directors can trust. Raw data fed into a model without that preparation produces outputs that may be unreliable — and unreliable outputs are unacceptable when the decisions are consequential.

What institutions need is trusted context. Data that has been curated, normalized, enriched, and structured so that AI systems can reason over it correctly and produce results that hold up when it matters most.

That is what Moody’s provides – data spanning domains and geographies, transformed with the right taxonomy and standardization, and powered by decades of expertise, rigor, and institutional knowledge. As AI becomes the primary interface for risk and financial decisions, this context is not optional, it is the essential input.
 

Moody’s understands risk – AI does not change that

Moody's is not just a rating agency or information data provider. We are among the world's most trusted intelligence networks, now available at AI scale. For the first time, decades of Moody's analytical expertise, proprietary data, and risk models are being structured and embedded into AI at the moment of decision.

Data has long been the key to unlocking insight on risk — and Moody's built one of the world's most trusted, most comprehensive risk intelligence franchises on that foundation. Now, with AI becoming the preferred interface for information gathering and analysis, Moody's is well positioned to lead because we have turned that data into an actionable context layer that helps the world's most powerful AI systems reason across vast and complex datasets.
 

Scaling our context layer: Moody’s core capability

At the center of Moody’s offering is the development of our context layer—a continuously evolving, structured representation of global financial risk built from Moody’s data, models, and more than a century of risk expertise. It will provide the intelligent analysis of raw data assets for AI reasoning engines, making data usable for the kind of rigorous, high-stakes reasoning that institutions require.

The context layer is not a database. It’s what gives data real meaning: how it relates across entities, time, and scenarios, and when and why it should be applied. It represents a deeply structured understanding of financial reality that cannot be reconstructed by scraping the Internet or prompting a general-purpose model. Three properties make it genuinely irreplaceable:
 

1. It is irreversibly derived

The underlying sources of much of Moody’s data may be accessible to others in raw form. What is not accessible is the transformation logic—the entity resolution, ownership inference, semantic normalization, scenario frameworks, and assumptions—that Moody’s applies to produce the outputs. Our datasets are the product of those transformations.

But irreversibility runs deeper than process. It reflects the compounding effect of decades of investment in how raw information is interpreted, structured, and connected. Each transformation layer builds on the last, so as methodologies evolve, earlier choices continue to shape how new data is ingested – compounding into a structured knowledge base with depth and internal consistency that cannot be recreated from scratch. As underlying AI models become increasingly powerful and increasingly interchangeable, the durable differentiating factor shifts to the intelligence layer beneath them. That layer is ours.
 

2. It is built on and curated with high degrees of analytical rigor

Moody's global company database illustrates this clearly. It is not simply company data—it is the product of years of curation: painstaking entity resolution, ownership mapping, and human judgment applied across jurisdictions to create a result that is structurally impossible to replicate by simply scraping public sources. The data Moody's holds include information that is assembled through a complex web of commercial relationships built over decades—all of which then supports a network effect across 600 million entities and 2 billion ownership links.

That depth of curation is what makes the data highly valuable to the institutions that depend on it most. Regulated entities—banks, insurers, asset managers, even governments and corporates—cannot build on a foundation they do not trust. They need data with clear provenance, documented lineage, and the kind of rigorous quality standards that only come from sustained, deliberate investment. Moody's meets that bar and will continue to evolve its data to do so in the future.
 

3. It is permanently enriched by accumulated human judgement

Moody's data assets are not simply collected, they are continuously shaped by experts whose accumulated methodological judgment is itself embedded into the data. That embedded judgment is what makes Moody's intelligence decision-grade. It reflects not just what the data says, but what it means, how it should be interpreted, and what quality thresholds it must meet for high-stakes financial decisions. General-purpose AI trained on public data has no access to that embedded expertise and cannot reconstruct it.

That data enrichment includes the judgment calls embedded in entity resolution, the curation decisions that govern how relationships are defined, how anomalies are resolved, and how new information is integrated without corrupting historical records. It reflects decades of accumulated methodological knowledge about what data matters, how it should be structured, and what quality thresholds it must meet for high-stakes financial decision-making. This is knowledge that cannot be extracted from the internet or approximated by a general-purpose model
 

The knowledge graph: Intelligence that compounds

Moody's is building the world's most comprehensive risk knowledge graph - connecting 600 million entities, 2 billion ownership links, ratings, credit scores, catastrophe models, CRE data and more - into a single intelligence fabric.  The result will be a comprehensive knowledge graph: an interconnected representation of relationships, exposures, and risk signals across the global economy.

Across every major domain of financial risk — credit, physical, financial crime, cyber, economic, and more — the knowledge graph will deliver a 360-degree view of risk that cannot be reconstructed from public data, replicated by a general-purpose model, or shortcut by any competitor starting from scratch today.

This knowledge graph will be massive, unique, and proprietary—reflecting Moody's data and models in a way that creates a true differentiator in an AI-driven market. It is being constructed from decades of expertise that generic AI cannot access or replicate.

This is not a point solution. It is foundational infrastructure — the same way technology companies provide infrastructure for software applications, our connected intelligence is the infrastructure for high-stakes decision-making. Every new dataset added to the knowledge graph increases the insight value of every other dataset already in it. And as AI systems become more capable of reasoning across complex networks, the value of a well-constructed, trusted network grows with them.
 

Bringing intelligence to customers where they are

Moody's value is not only our data and tools — it is how we connect them to embed intelligence directly into customer decision-making, wherever that decision-making happens. We are partnering with the world's leading AI reasoning platforms to embed our connected intelligence directly into the tools and environments where customers already work.

Each partnership is not a standalone integration. It is a new access point into one connected system — the same knowledge graph, the same trusted context layer, the same decision-grade intelligence — delivered through whichever AI interface a customer is building on. The system doesn't change. The access points expand.

Delivery operates across three channels simultaneously:

→ Direct MCP distribution – making Moody’s connected intelligence available as AI-ready inputs through Model Context Protocol (MCP) servers delivered directly to customers building their own agentic platforms and workflows.

→ MCP integration through third-party platforms – embedding Moody’s intelligence into leading enterprise AI and data platforms, so customers can access Moody’s capabilities within the ecosystems they already use.

→ Moody's own agentic solutions – purpose-built AI workflows that deliver decision-grade intelligence directly to customers, without requiring them to build the surrounding infrastructure themselves.
 

This distribution architecture does not compromise Moody’s intellectual property. Proprietary data, methodologies, and the trillions of linkages in the knowledge graph will remain fully protected regardless of delivery channel. Access is governed by permissions and full auditability. Moody’s will not surrender control of the context layer we are scaling in order to meet customers where they are.

The architecture reflects a key insight: customer needs vary significantly by complexity and scale. The most advanced institutions – with established data infrastructure and the engineering capacity to build their own workflows – will want Moody’s content embedded directly into the platforms they have constructed. For a broader set of institutions, the greater opportunity lies in Moody’s domain-specific agentic solutions: purpose-built workflows that deliver the same intelligence without requiring customers to build the surrounding infrastructure themselves. Moody’s is designed to serve both.
 

Why this matters for regulated institutions

The question facing every regulated institution today is not whether to adopt AI. It is how to adopt AI in a way that is defensible — to regulators, to boards, to auditors, and to the counterparties that depend on the quality of their decisions.

General-purpose AI models can present a specific problem for large, regulated institutions: outputs are difficult to audit, data provenance is opaque, and whether a model has been trained on confidential or legally restricted information is genuinely unresolved. These are not hypothetical concerns. They are material compliance and governance issues for every institution operating in regulated markets. A bank deploying AI in credit underwriting must be able to explain to its regulator exactly what data informed every decision while demonstrating that the underlying data meets documented quality and provenance standards.

Moody's has an answer to that governance challenge. The combination of credentialed intelligence, documented data provenance, and explainable outputs is not a compliance feature — it is a differentiator for institutions that need to defend their AI-driven decisions to the people and institutions whose financial wellbeing depends on getting them right. When the world's leading AI companies choose to ground their financial services capabilities in Moody's intelligence, they are not purchasing speed. They are purchasing clarity, confidence, and defensibility. That is not a transaction — it is a validation of what we have built.
 

The trusted context

As AI proliferates, the question of who wins is not who has the most capable model. The question is who controls the trusted context that makes models useful for consequential decisions.

Moody’s foundation will be built on the convergence of a knowledge graph constantly compounding, a context layer built on over a century of expertise, and a distribution architecture capable of embedding Moody’s intelligence into every workflow. Each pillar is difficult to replicate; together they are a stack of compounding advantage for Moody’s and fundamental infrastructure for companies: methodologies, governance frameworks, institutional memory, and embedded workflows that are a critical part of how an institution operates.

Moody’s is not racing to become an AI company. We are positioning the intelligence we have always provided—trusted, independent, decision-grade—as the essential input that AI-driven decision-making requires. Technology is changing, but the need for trusted, connected intelligence is not. Moody’s is uniquely positioned to provide it at the scale AI requires.
 

“Safe Harbor” statement under the Private Securities Litigation Reform Act of 1995

^{Certain statements contained in this document are forward-looking statements and are based on future expectations, plans and prospects for Moody’s business and operations that involve a number of risks and uncertainties. Such statements involve estimates, projections, goals, forecasts, assumptions and uncertainties that could cause actual results or outcomes to differ materially from those contemplated, expressed, projected, anticipated or implied in the forward-looking statements. Stockholders and investors are cautioned not to place undue reliance on these forward-looking statements. The forward-looking statements and other information in this document are made as of the date hereof, and Moody’s undertakes no obligation (nor does it intend) to publicly supplement, update or revise such statements on a going-forward basis, whether as a result of subsequent developments, changed expectations or otherwise, except as required by applicable law or regulation. Factors, risks and uncertainties as well as other risks and uncertainties that could cause Moody’s actual results to differ materially from those contemplated, expressed, projected, anticipated or implied in the forward-looking statements are described in greater detail under “Risk Factors” in Part I, Item 1A of Moody’s annual report on Form 10-K for the year ended December 31, 2025, and in other filings made by the Company from time to time with the SEC or in materials incorporated herein or therein. Stockholders and investors are cautioned that the occurrence of any of these factors, risks and uncertainties may cause the Company’s actual results to differ materially from those contemplated, expressed, projected, anticipated or implied in the forward-looking statements, which could have a material and adverse effect on the Company’s business, results of operations and financial condition.}