S&P Global Credit Analytics Head Says Faster Credit Decisions Start With Embedded Risk Data

The way enterprises source and act on risk data is changing. Functions that once bought separate function-relevant information now want to know as much as possible about their clients and vendors—they want cyber, financial, and operational indicators, and they want them inside the platforms where decisions get made, rather than in separate systems. That convergence pushes CIOs to balance the need for more data with the requirement for fewer vendors, and it is redefining what good data, analytics, and workflow integration look like.

Andy Armstrong, Head of Business and Market Strategy for Credit Analytics at S&P Global, oversees a portfolio of risk products spanning customer credit, supplier and third-party financial risk, and transfer pricing. He brings more than two decades of experience building and scaling data-driven subscription businesses, with prior leadership roles at CEB, Gartner, and Mercer. Armstrong said what struck him most, working across thousands of client relationships, was how quickly the data needs of different risk-related functions have started to converge.

"More and more clients are looking for more—and more diverse—sets of data. Supplier risk and credit risk used to ask for completely different data. Supplier risk focused on compliance, cyber, and general financial stability data. Credit risk focused on markers of financial instability like credit scores, lending limits, and, depending on the segment, location and climate-related risk. Now both functions are asking for all of the above," said Armstrong.

The convergence he described is reshaping how enterprises think about data procurement. Risk functions that were once made narrow, separate requests now arrive at the same table with overlapping needs, looking for a single source to cover them all. Meeting that demand means connecting more data sources to more internal systems, and that is where the integration challenge begins.

Mapping APIs by hand is an operational headache. Connecting disparate systems requires people who understand both the supplier's data architecture and the client's, and even when the pipeline goes live, integration mapping costs rarely stop there. The wrong data landing in a vendor evaluation creates governance risks that are hard to detect, and harder to unwind. That is why many teams are standardizing how risk data is packaged and delivered through a more flexible, data-agnostic architecture that reduces vendor lock-in, rather than rebuilding integrations from scratch. S&P Global's whitepaper on connectors for supplier and credit risk breaks down how pre-built, use-case-specific data packages reduce integration overhead and accelerate time-to-value for risk teams.

• Context over code: For a credit analyst or supplier risk leader, a raw number is only the starting point. Interpreting the geographic, sector, and company-specific drivers behind it, the context that has to be mapped into the end system from day one. Pre-built connectors handle that by combining integration with domain-specific packaging. "If you're sitting in the seat of a head of risk or a credit analyst, a score is an indicator—most analysts (especially for their top tier suppliers or credit counterparties) will want to dive into the drivers behind the score; that’s true during vendor assessment or onboarding, but particularly true when monitoring existing partners, if a score changes…you need to know why," Armstrong said. "If you're looking at this solely from a technology standpoint, you might not know that you need to map that contextual information into your end system."

A 60% faster time-to-value is a meaningful number for an IT organization, but Armstrong said the question is whose value it is measuring. Getting a pipeline live is one milestone. Getting business teams to act on the data inside their daily workflows is another. Moving data into a centralized lake handles the first, but it frequently leaves the second entirely to the business unit. Pre-built connectors combined with data packages constructed for specific use cases like supplier risk or credit risk close that gap by delivering packaged intelligence directly into end-user workflows rather than simply collecting data in a single place.

• The A-to-Z test: The real measure of integration success is not whether the data is live. It is whether the business teams relying on it can make faster decisions. "The first thing that comes to mind is: whose value? What is the measure of value?" Armstrong said. "For an IT organization mapping APIs, time-to-value might simply mean the tool is ready to use. That, to me, is like going from A to P. The A-to-Z is: Is it actually driving the decisions my internal clients need it to drive? We get you faster from A to Z than just mapping an API because we link scores with drivers, company financials, sanctions, and compliance info…it’s about getting the right information into the place where a decision gets made."

For risk teams assessing small, private entities, there may be no existing score to pull. S&P addresses that gap by exposing its analytical methodology through connectors, turning the data a client holds on an unrated counterparty into a score on demand. The same connector infrastructure also serves as the delivery mechanism for S&P's broader data strategy, giving CIOs a flexible channel that fits into existing workflows without requiring custom builds.

• Math, not magic: If a procurement team has proprietary access to a third party’s financials (or even a subset of them), or worst case scenario, just their industry and geography, that information can pass through the connector and return an analytically sound score within seconds, inside the system where the analyst is already working. "We have scores for tens of millions of companies around the world; if there’s a vendor, say, that is new, or is private and doesn’t report financials, and we don't have a score for them yet, we enable the client to score it directly through the connector, via the platform in which they’re already working – with some basic information about that particular counterparty, you can send us that data via the connector and we can score that entity and automatically push that score back in the blink of an eye," Armstrong said.

• Plumbing over poetry: Connectors serve as a foundational data readiness layer for higher-level automation, surfacing required inputs inside existing workflows rather than forcing analysts to pull reports or copy data between systems. Armstrong noted that connectors are not an AI product. They are the infrastructure that makes advanced analytics projects useful in practice, freeing teams from the judgment calls and exception handling that cannot be automated. S&P delivers the same underlying data through multiple channels, including desktop platforms, APIs, connectors, MCPs, and model-oriented interfaces, so clients can plug it into different strategies, potentially including those built on emerging AI initiatives. Armstrong said, "We look at connectors as one of many ways we have to help clients get access to the information they need to make decisions."

For CIOs, Armstrong framed the decision as one of efficiency: how do we enable risk, credit, and procurement leaders to access the most comprehensive, best risk-related information from a single source? S&P, which sits on an unparalleled set of risk data, is in a unique position to offer tools that combine pre-packaged, use-case-specific content—like supplier management or credit risk management—with plug-and-play distribution and governance capabilities, reducing the number of custom builds CIOs have to oversee. As organizations develop internal playbooks for owning that embedded risk intelligence, the CIO's role shifts from builder to advisor—in this case, finding a company like S&P that can supply both the most credible data and the easiest way to ingest it.

In most organizations, budgets for embedded risk intelligence sit with the functions facing the immediate pain: supply chain, procurement, credit. CIOs focus on governance, security, and architectural fit. The technology to embed risk intelligence directly into workflows is already available. The buying patterns and ownership models are still catching up, and that gap gives CIOs, along with their counterparts in supplier risk, procurement, and credit, room to shape how tools like connectors get adopted and how quickly risk decisions can move from data to action.