Leveraging AI for Efficient Credit Memo Preparation

The need to work faster and smarter has never been more important in today’s environment of rapidly changing market conditions. AI has risen to the top of company agendas as a versatile tool to speed up analysis and quickly distill relevant information into actionable insights. How will AI impact the highly manual and time-consuming task of preparing credit memos?

S&P Global Market Intelligence recently conducted an email survey^[1] with senior credit risk professionals at 39 firms around the world to better understand their views on the use of AI to improve turnaround times and efficiencies with credit memos. The firms included a mixture of asset managers, commercial and investment banks, insurance companies and corporations.

Profiles of Survey Participants

Team sizes varied among survey participants. Over one third (34%) had smaller teams with just one to five people, over half (53%) had six to 15 people and the remaining (13%) had very large groups with 16 to 50 people.

The number of credit memos done per week also varied by firm. While 46% of participants did fewer than 10 credit memos each week, 34% did 10 to 25, 10% did 26 to 100 and another 10% did more than 100.

Many participants (41%) said credit memos are managed in a very manual process today, using tools such as Microsoft Word and Excel. One third (33%) said they use a combination of AI and manual approaches. Some (13%) have built custom systems internally, and a small number (5%) are using AI-enabled tools, such as Microsoft Copilot’s generative AI chatbot, or third-party systems. The use of broader internal platforms is negligible (3%).

Having different approaches for credit memos used across teams and regions creates governance headaches and quality control issues. Over half of participants (54%) said they have a standardized approach throughout the organization. The remaining 44% said the approach was only partially standardized by team or region.

Many Face Challenges

Only 8% of those surveyed said they are extremely satisfied with their current approach for generating credit memos, while 67% were either somewhat satisfied or somewhat/extremely dissatisfied. One quarter (25%) were neutral, neither feeling satisfied nor dissatisfied. 

When asked about their single biggest challenge, almost half (46%) said it was too manual and time consuming, while others (18%) complained about inconsistent assessment quality or structure and still others (8%) about limited insight, analytics or reuse of analysis. About one quarter (23%) said they didn’t face challenges today.

Views differed on build versus buy to create a better solution. Over one third (36%) said they are open to considering third-party solutions, while just under one third (31%) indicated that they prefer to build and maintain internally. A small number (8%) are in the process of actively evaluating vendors, while one quarter (25%) are not considering changes at this time.

Perceived Benefits of AI

Almost two thirds of participants (64%) said AI was important or critical to move their credit analysis forward, while the remaining one third (36%) described it as a nice to have or not important today.

Many participants (67%) agreed that AI would introduce efficiencies and make it faster to draft credit memos. Others (42%) saw benefits from the time-consuming task of aggregating and synthesizing data from multiple sources, while one quarter (24%) said it would be easier to do financial analysis or ratio calculation and identify risk flags or early warning signals. About one fifth (21%) also saw benefits from improved consistency across teams and regions as companies try to standardize approaches.

A Data-intensive Exercise

Data is drawn from many sources to support analysis. Aggregating, synthesizing and making inferences can take hours. The following sources are typically used.

Embracing AI

This outreach with strategic clients revealed an escalating challenge: how to process more information in less time while maintaining rigorous standards. Manual credit memo preparation consumes hours, pulling analysts away from higher-value assessments. The process is not only time-consuming but may also be inconsistent across teams, creating governance and quality control issues. With almost two thirds of participants (64%) saying AI was important or critical to advance their approach to credit analysis, credit memo preparation is likely to look very different in the months ahead.

^{[1] Credit Memo Survey. S&P Global Market Intelligence. 13 April-14 May 2026.}