Credit data provider of the year: Moody’s

As financial institutions seek faster, more contextualised credit insights, Moody’s has combined vast proprietary datasets, domain expertise and agentic artificial intelligence tools to help clients make more informed risk decisions

AI is reshaping financial services, and the value of data is increasingly judged by context as well as volume. For credit risk professionals, the challenge is to turn vast quantities of data into timely, actionable intelligence.

That challenge defines Moody’s proposition and helped secure its recognition as Credit data provider of the year in the 2026 Risk Technology Awards.

With more than a century of experience in credit assessment, Moody’s has built a reputation on its proprietary ratings, research and market expertise. Over the past 12 months, however, the company has focused on combining those traditional strengths with advanced analytics, agentic AI capabilities and one of the industry’s largest collections of private company credit data.

Moody’s data ecosystem spans more than 630 million companies worldwide, incorporating demographic, financial, ownership and environmental, social and governance (ESG) information, alongside proprietary ratings and research. The firm’s Data Alliance consortium, which includes more than 100 financial institutions, provides access to more than 114 million private company financial statements, $588 billion of commercial real estate loan data and a substantial share of global project finance lending activity.

These datasets support a wide range of use cases, including International Financial Reporting Standard 9, known as IFRS 9, and current expected credit loss, known as CECL, modelling, as well as stress-testing, benchmarking, portfolio monitoring and credit decision-making.

But scale isn’t the only part of Moody’s offering.

“The problem isn’t a lack of information – it’s that we’re overflowed with information,” says Cristina Pieretti, head of digital content and innovation at Moody’s. “There’s so much data from different sources that sometimes can be trusted or not trusted. You have to decide what data you’re going to leverage, which signals you’ll pay attention to, and which you should disregard.”

The firm’s differentiation lies in the combination of data, analytics and domain expertise. Moody’s seeks to embed market knowledge and credit judgement directly into its workflows, models and client-facing tools. This approach has become increasingly important as organisations experiment with generative AI (GenAI) and agentic AI technologies.

A recent milestone has been the launch of Moody’s OneView, a unified platform designed to bring together risk assessment, data assets and analytical capabilities within a single environment.

The platform provides access to financial statements, ownership structures, corporate profiles, credit research and benchmarking tools covering both public and private companies. Users can analyse financial performance, compare peer groups, monitor emerging risks and assess creditworthiness through configurable scorecards and probability of default metrics. The aim is to reduce fragmentation and give analysts a more complete view of risk within a single workflow.

“You can’t look at credit risk as a standalone any more – things have become more interconnected,” Pieretti notes. “If you only analyse a company in isolation you can miss the impact of one degree of separation: tariffs or a geopolitical event hitting suppliers can raise a company’s cost of goods and squeeze margins. It’s not about one piece of information, but all the related parts that could meaningfully impact a company’s credit profile.”

The solution also incorporates broader market signals, including sector research, macroeconomic analysis, ESG indicators, news sentiment and capital structure data. Together, these capabilities allow institutions to move beyond static financial analysis and incorporate a wider range of risk factors into their decision-making processes.

Alongside Moody’s OneView, the firm has expanded its investment in agentic AI solutions.

Two flagship offerings – the agentic Credit Memo and Scorecard Agent – are designed to automate some of the most time-consuming activities within credit assessment workflows.

Credit Memo automates the collection of company disclosures, supports memo drafting and assists with regulatory documentation requirements. Built-in citations and source references help maintain transparency and auditability, while users retain the ability to customise outputs and incorporate their own data sources.

AI can make our work faster, more efficient and allow us to do more – for example, more lending or investment decisions and more real‑time monitoring of exposures

Cristina Pieretti

Meanwhile, Scorecard Agent automates financial data extraction and scorecard preparation, producing indicative assessments aligned with established methodologies while preserving human oversight and review.

The emphasis on human oversight is key. These tools offer a way of reducing manual effort and accelerating decision-making while maintaining governance and accountability.

“AI can make our work faster, more efficient and allow us to do more – for example, more lending or investment decisions and more real‑time monitoring of exposures,” Pieretti says. “But it can also surface insights an analyst might have overlooked. The challenge is ensuring credibility of the source: everything needs to be traced and auditable so users can confirm veracity and avoid automating in the wrong places.”

While institutions are seeking greater efficiency, they are increasingly looking for solutions that combine automation with explainability. The answers must be understood, validated and defended.

Looking ahead, Moody’s expects demand for contextualised credit intelligence to increase as AI adoption moves from experimentation into production environments. Economic uncertainty, geopolitical volatility and the continued expansion of private credit markets are likely to increase the need for predictive analytics and early warning indicators. Organisations will also require greater confidence in the data, methodologies and expertise underpinning AI-generated outputs.

“When leaders think about the risks to watch over the next 12 months,” Pieretti says, “they’re looking at how GenAI could change a company’s market position, geopolitical events, cyber risk and climate impacts. More important than any single risk is that they’re increasingly interconnected – you need to look at owners, customers, suppliers, where they’re located, and how that whole web can affect viability.”

For Moody’s, that represents an opportunity to build on more than a century of credit market experience while adapting its products for a new generation of workflows. By combining extensive datasets, proprietary analytics and embedded domain expertise, the company is seeking to ensure that, in an increasingly automated world, better decisions remain grounded in trusted intelligence.

“Private credit is expanding into more counterparties and a wider range of credits, often in opaque markets,” Pieretti says. “That makes the quality, veracity, depth and connectedness of information even more important – and increases the need not only for thorough initial analysis, but continuous monitoring over time.”