BLOG — July 16, 2025
AI in private credit: Raising the bar for STP
AI is transforming private credit by accelerating the workflow and supporting new capabilities and insights across the lending lifecycle. The first wave of AI drove a 20% productivity gain: the next wave promises to deliver even greater returns.
This blog series explores the areas where AI is having the greatest impact in private lending—automation, data extraction, the user experience and reconciliation.
Given that the global private debt market is expected to reach $3.5 trillion in AUM by the end of 2028, advances in automation and straight-through-processing (STP) can't come soon enough.
While the one-to-many structure of broadly syndicated loans (BSLs) lend themselves to scalable processes, private credit’s non-standard, highly customized agreements have always required a manual, bespoke approach. The result is a uniquely agile and competitive loan product that continues to attract investors and borrowers at scale, but one that is also uniquely resource-intensive in terms of processing and management.
Traditional automation technologies have only gone so far in streamlining the private loan lifecycle, but AI is breaking through the barriers to enable unprecedented levels of STP and faster, better decision-making.
The AI accelerator
Generative AI (GenAI) has proven itself capable of bringing more structure, efficiency and transparency to the private loan lifecycle while reducing the amount of human intervention required to process credit agreements and agent notices.
Over the past year, S&P Global Market Intelligence has integrated GenAI into the processes managed by our WSO services teams for private credit. AI is helping them tackle high volumes of complex processes— such as adjusting amortization schedules based on pre-payment notes or effecting many-to-one and many-to-many cash matching to automate cash reconciliation —at previously impossible speeds.
Our team leverages proprietary GenAI models to execute the categorization of activity and extract key data, including the details of the agent sending the notice and the type of notice received (rollover, rate reset, interest payment, failure to pay or a revolver draw-down). As a result, cash reconciliation has been completed three days after the close of the quarter end—the highest level of efficiency ever achieved.
Applying GenAI to these processes significantly reduces the manual adjustments and interventions that ordinarily interrupt the workflow. In the first quarter of 2024, immediately prior to the rollout of our AI technology, our services team maintained a 5% STP rate. A year later, 50% of transactions are STP, with peak efficiencies exceeding 60%.
These new efficiencies have enabled our team to support 200+ customers and an astonishing volume of notices daily—more than two million on a quarter-end basis.
Continuous learning and AI agents
In a highly contextual, nuanced processing environment, AI's ability to analyze, learn and improve is one of its most valuable qualities. The AI models supporting our WSO services team are continuously learning and improving, which will allow us to achieve even higher STP rates over time. Our target is to reach over 95% notice categorization rates and over 70% STP data extraction and processing from faxes and PDFs in 2025.
We are also launching an AI agent that can supply team members with additional context for exceptions or unexpected data points. Our team will be able to query the credit agreement with natural language and receive instant responses that link back to the supporting document and indicate the specific page from which the data was sourced. This functionality is especially valuable in cases where multiple amendments are in play and will help to resolve the most resource-intensive issues quickly and definitively.
A massive step forward in STP
One of our most ambitious initiatives is slated to launch in the next six months and will take our STP efficiency even further. This initiative will enable us to connect directly to agents and receive credit agreements and agent notices on behalf of our clients. This data hub will accelerate processing and minimize the resourcing required to resend thousands of notices each month.
The data can then be delivered to our clients in both structured and unstructured formats so that they can retain more traditional formats such as PDFs and faxes (for audit purposes, for example) or ingest the structured data seamlessly into their downstream processes. This digital approach will massively increase the rates of STP automation and allow for faster reconciliation and end-of-month/end-of-quarter closes and investor reporting.
The future of AI-powered STP
Applying AI solutions to the private loan lifecycle is already delivering measurable process improvements to our clients, and these early results are only the tip of the iceberg. As our AI models continue to evolve, and as we roll out refinements, such as AI agents and more direct loan data ingestion, the share of the loan lifecycle that can be processed without human intervention will continue to climb. Private credit is closer than ever to achieving the levels of speed, efficiency and visibility that more standardized asset classes enjoy.
Read the other installments in our AI in lending blog series: