AI in Private Credit: Surfacing Deeper Data Insights

The data embedded in private credit agreements and agent notices is crucial to the loan management process, but extracting and utilizing it has always required intensive resources. Lending agreements can exceed 300 pages of complex material, and locating and extracting the data manually can take hours. And because of the one-to-one or one-to-few nature of those agreements, scaling data extraction poses a unique challenge for this asset class.

First-gen technologies, such as batch processing or methods based on scripts, rules or OCR, provided limited relief, but AI represents a quantum leap forward. And its application to private credit is not just hypothetical: S&P Global Market Intelligence is already using AI to extract and analyze structured, unstructured, textual and visual data from loan documents with unprecedented speed and accuracy.

Accelerating data extraction

AI is now fully integrated into the work we deliver to our clients in private credit. WSO Software, a platform that delivers portfolio insights, workflow automation, and deal administration for diversified credit portfolios, CLOs, and syndicated and leveraged loans, is equipped with an AI extraction tool that identifies and captures key terms. Our Lending Solutions team are now applying natural language processing (NLP) and large language models (LLMs) to automate the extraction, normalization and validation of data from credit agreements and agent notices.

This functionality reduces manual review time, minimizes errors and accelerates downstream processing to drive significant efficiency gains across the lifecycle. It is also creating the largest and most comprehensive database of private and broadly syndicated loans in the industry.

Our team digitizes over 23 million agent notices annually on behalf of our clients; more than 90% are now auto-labeled and categorized during peak periods with a >99% process accuracy rate. The amount of data that can be extracted and the accuracy of that data is improving exponentially; OCR capture in 2025 increased from 76% to 87% YOY and the average error rate is now <1.37%. 

Leveraging AI tools in 2025 have enabled S&P Global Market Intelligence to extract 80+ data elements, including issuer name, country, maturity date and agent name from sources such as notices, credit agreement, amendments and other loan documents, and to make that data available to downstream processes via APIs.

Monitoring loan activity

AI data models can also be used to automate the process of interpreting the treatment of unscheduled paydowns and determining how they affect the amortization schedule. This approach streamlines the process and improves the accuracy of the model’s predictions. 

A credit agreement is constantly being referred to for covenant information, spread changes, and repayment and interest activities. AI is helping us to create systems that allow quick querying of the data when loan activity has optionality and the credit agreement must be referenced. This will transform our efficiency and accuracy in handling situations such as early prepayments, enabling us to verify if a prepayment is allowed, how the prepayment affects the amortization schedule and how PIK or interest is impacted with reference to the original credit agreement. 

AI monitoring case study

During LIBOR transition, many S&P Global Market Intelligence clients were unsure of what their risk exposure looked like within existing credit agreements. Our services team was able to quickly spin up a new dataset to help them navigate the emerging situation, understand the risks and adopt new language to use in their agreements moving forward. Instead of going through tens of thousands of documents manually, AI enabled us to pivot quickly to accommodate a change in the industry and help our clients respond to regulatory changes, align with industry best practices and mitigate risk.

The future of AI-powered loan data

We are continually refining the way we collect and extract loan reference data, and we're also exploring the potential for using that data to generate additional market and loan insight.

Our security master initiative will centralize the data collected by the Loan Reference Data, WSO, ClearPar, and Loan Pricing teams and create a unified master dataset to deliver coordinated, normalized, golden-source private credit data to our clients, including trend analysis, new issues, ratings and spread buckets, and more.

As private credit faces one of its greatest opportunities to grow, AI is helping the asset class scale up operations in ways that were previously inconceivable. Our services and product roadmaps reflect the continued, incremental integration of AI into the loan lifecycle to ensure our clients have access to the technology’s powerful benefits, including access to fresher, more accurate, more complete loan data to power the workflow.

Read the other installments in our AI in lending blog series:

• AI in private credit: Raising the bar for STP
• AI in lending: Reshaping the user experience
• AI in private credit: Strengthening the reconciliation function | S&P Global