Case Study — May 29, 2026
A Bank Uses AI Backed by Trusted Data to Quickly and Reliably Produce Credit Memos
THE CLIENT:
A European-based international bank
USERS:
The credit risk management team
Private credit has emerged as a dynamic force in global finance.1 Driven by post-financial crisis regulatory constraints on traditional banks, nonbank financial institutions (NBFIs) have filled the lending void for small-to-medium-sized enterprises. As traditional banks now go on the offensive, it is imperative that credit analysts work more efficiently to assess deals at a faster pace. Reliable data and streamlined processes are needed to support quick assessments of creditworthiness that still must be comprehensive and auditable given the high degree of regulatory scrutiny in the banking industry.
Members of the credit risk management team at this large international bank had a very manual and time-consuming process in place for creating credit memos. The team wanted to investigate emerging artificial intelligence (AI) solutions that could help produce trustworthy memos with speed and precision.
AI solutions can help credit analysts save time assessing loan deals but must be backed by reliable data and credit expertise to help meet high standards of accuracy.
Pain Points
Members of the credit risk management team were spending many hours sifting through annual reports, rating research, macroeconomic information, news and key developments to support their foundational analysis before they could focus on the credit rationale and deal structure. They wanted to investigate AI solutions that could let them spend more time on analysis and look at deals at a faster pace. Ideally, a solution would also let them:
- Easily tap into trusted data sources to save time gathering/synthesizing information and evaluating the implications.
- Infer trends and drivers of key financial metrics by considering unstructured information, such as research reports and news.
- Complement the data with proprietary information, such as financial statements or content from other available sources.
- Surface credit relevant insights and generate analyst ready drafts in minutes.
- Have narratives that are auditable and evidence based, even in highly regulated environments.
- Enable analysts to retain full judgment ― including editorial control and final approval.
- Show clear traceability between data, conclusions and analyst judgment.
In addition to these core needs, any solution must be highly customized to meet the firm's unique and very detailed approach for assessing counterparty risk. Members of the team knew that S&P Global Market Intelligence ("Market Intelligence") was well known for its credit know how and extensive data. They reached out to learn more about the firm's AI initiatives.
The Solution
Market Intelligence has been involved in AI initiatives for many years, often building solutions with leading technology partners to scale innovation, improve data delivery and embed AI into financial workflows with precision. The firm's specialists described Credit Memo Builder™, a new AI-driven capability backed by S&P Global's advanced credit experience and trusted data that delivers credit memos fast and at scale. The capability provides:
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Fully customized credit memos that meet a firm's specific requirements |
Purpose‑built templates and section ideas in Credit Memo Builder enable users to easily customize the solution through a series of guided prompts. Market Intelligence also provides close support for more complex customization efforts to help users address their exact reporting needs and desire for additional analytics. |
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Faster and smarter end-to-end memo creation |
Credit Memo Builder quickly delivers automated first drafts and repeatable memo-ready summaries It pulls data from S&P Global's AI-ready datasets to reduce the time needed to collect and synthesize information and make inferences. The data includes industry reports, credit research, news and financial statements. It also includes a wide range of specialized data, such as RiskGauge Scores that provide a holistic assessment of a company's fundamental credit risk and market-based signals and S&P Global Ratings' well-known credit ratings. Proprietary information can also be securely uploaded for use across an entire memo or at a section level. |
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Reliability based on trusted S&P Global data |
AI needs more than just large volumes of data. It requires information that is consistent, explainable, enriched and ready for direct integration into workflows. S&P Global's data is:
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Thorough transparency and explainability |
Credit Memo Builder quickly surfaces credit-relevant data and articulates drivers and rationales. It helps decrease omissions and version drift, minimizing operational risk. |
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Analyst in the loop controls |
AI does the heavy lifting to source the right information and put it in the appropriate section of a memo, but analysts can use their judgment and run sanity checks on the overall narrative and prompt sections to expand, summarize or integrate with other documents as needed. |
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Assistance to validate results |
Credit Memo Builder minimizes the need to manually validate information. Analysts can:
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Key Benefits
Members of the credit risk management team liked Credit Memo Builder's initial out-of-the-box memo generation based on Market Intelligence data. This can then be enhanced with proprietary documents and data and further refined by using analyst-prompting to dive into a specific company’s risk assessment. They saw the ability to save hours of time without relinquishing quality and valued the opportunity to work closely with Market Intelligence specialists to fully customize the solution and push it as far as possible to see what analytics were available.
The team also valued the ability to:
- Leverage AI to consistently tap into a full range of sources, rather than having an analyst choose where to look for information based on their available time.
- Refresh a memo with a click versus having to repeat the overall process, providing the ability to monitor deals in a way that was not possible before.
- Trust narratives that are evidence‑based and traceable to source data with clear linkages between data, conclusions and analyst judgment.
- Introduce a more consistent approach across analysts to meet the firm's unique analysis and generate review-ready results.