BLOG — Oct 18, 2024
Credit Memo Automation Case Study
By Michelle P. Cheong, Shruthi Nagarajan, Katriona Ho, Kyu Yeon Lim, and Andy Chu
Combining Content sets within RatingsXpress®, Credit Analytics and S&P Global Market Intelligence
Problem Statement:
As part of their Digital Transformation efforts, clients are seeking to automate the creation of internal Credit Memos for use by their credit analysts, capital markets, loan approvals, and other relevant business units. S&P Global Market Intelligence is uniquely positioned to deliver such solutions and essential intelligence, offering market participants comprehensive digitized content on credit risk, with flexible platform delivery options, seamlessly cross referenced through unified and proprietary entity identifiers.
Components of an Automated In-house Credit Memo
A dynamically and regularly updated credit memo typically include the following:
- Descriptive information
- Key Personnel and Ownership Information
- Credit ratings and research
- Forecasted probability of default and credit scores
- Deep sector company financials (e.g. SNL FIG financials)
- ESG assessments and commentary
- Security prices and trends
- Clients' internal data
- Charts and graphs etc.
See Charts 1, 2, and 3 illustrate some datasets commonly found in credit memos. For a comprehensive list of datasets offered by S&P Global, please refer to https://www.marketplace.spglobal.com/en/datasets
Chart 1: Visualization of an entity’s descriptive information, financial statements and transactions history from S&P Global’s data delivery solutions
Source: S&P Global Market Intelligence, as of September 2024. For illustration only. Graphics are blurred out to anonymize the entity. The underlying data is available via XpressfeedTM, XpressAPI and Xpresscloud delivery platforms.
Chart 2: Visualization of RatingsXpress® Research Sections from S&P Global’s data delivery solutions
Source: S&P Global Market Intelligence, as of September 2024. For illustration only. Graphics are blurred out to anonymize the entity. The underlying data is available via XpressfeedTM, XpressAPI and Xpresscloud delivery platforms.
Chart 3: Visualization of Credit Analytics’ probability of default and scores, together with a chart of company fundamentals from S&P Global’s data delivery solutions
Source: S&P Global Market Intelligence, as of September 2024. For illustration only. Graphics are blurred out to anonymize the entity. The underlying data is available via XpressfeedTM, XpressAPI and Xpresscloud delivery platforms.
Desirable characteristics of data content and platform delivery
The table below describes some key stakeholders working to create automated credit memos and their frequently requested data attributes
Stakeholder |
Business Units |
Data Management & Governance |
Technology |
Frequently mentioned requirements |
· Consistent and broad data coverage – fewer unintended blanks in a memo · Data updated at sufficient frequency |
· Standardized data · Clear metadata labels · Data quality and integrity – as the data is automatically disseminated across the organization for decision making · Easy-to-use and intuitive data models · Machine readable data |
· Low effort to ingest data - fits clients' own data pipeline · Cross-referencing - into clients’ own entity, security, and industries · Flexible delivery – allow clients to choose the best-fitting distribution solution · Data security |
Source: Anecdotal evidence from client outreach. Credit Solutions Thought Leadership in consultation with Solutions Architects and Technology. As of September 2024. For illustration only.
Content Spotlight: RatingsXpress® Research Sections
To lower the effort or data ingestion, the sections of credit ratings research reports are databased as separate text fields (i.e., separate blurbs). This allows the end user to easily retrieve the executive summary sections commonly found on the first page across their universe, for example.
Chart 4 shows an extraction of “Credit Highlights” and “Outlook” of a sample universe on our Cloud-based workbench (more information here).
Chart 4: Illustration of how specific sections of multiple entity reports can be retrieved in bulk on our Cloud-based Workbench platform
Source: RatingsXpress® Credit Research Sections as of September 2024. For illustration only. Data is available via XpressfeedTM, XpressAPI and Xpresscloud delivery platforms.
Content Spotlight: Consolidated Delivery of Credit Ratings
We currently have S&P Global Ratings, Moody’s, and Fitch Ratings on our data delivery platforms as separate data packages ready to be ingested into credit memos. In our coming product releases, we will be launching a unified feed of credit ratings from S&P, Moody’s, and Fitch Credit Rating Agencies (CRAs). A benefit for the single feed is not having to handle separate data models and synchronize separate data streams. Additionally, the data fields are standardized across ratings agencies for easy comparisons .1
Content Spotlight: Credit Analytics’ probability of default and credit scores
Chart 5: Illustration of the comprehensive RiskGauge (RG) scores and forecasted Probability of Default (PD%) values via Xpresscloud, including risk drivers and contributions from Market and Fundamental analytics for both Rated and Unrated entities based on publicly disclosed information.
Source: RiskGauge via Xpressfeed as of September 2024. For illustration only. Data is available via XpressfeedTM, S&P Capital IQ API, API Drive, ClariFI®, Web Services, Marketplace Workbench, and Snowflake delivery platforms.
For benchmarking for unrated entities2, the Credit Analytics product suite, including our RiskGauge (RG) Credit Scores and Bond Implied Scores (BIS), leverages market pricing and fundamentals driven analytical models that deliver probability of default ranging from 0% to 100%, as well as lower-case credit scores that broadly align with ratings from S&P Global Ratings (more information here).
The BIS model is a dynamic statistical model which is driven by observed bond market prices or spreads. It facilitates an easy, efficient, and cost-effective evaluation of credit quality for global corporate bond issuers. It provides a channel to detect early warnings of credit events based on bond-derived signals. This is currently offered on the desktop but will be made available by Xpressfeed, Snowflake and S&P Capital IQ API (GDS API) in the very near future.
Content Spotlight: Business Entity Cross Referencing (BECRS)
Most if not all credit memos use multiple data sources. A very frequently used component to reduce the burden of cross referencing is S&P Global Market Intelligence’s Business Entity Cross Reference Service, which provides immediate cross-reference capabilities for millions of public and private entities. This saves costly resources and time to manually maintain your own mappings (more information here).
Solution Design, Implementation & Support: S&P Global Market Intelligence Solutions Architect team and 24/7 client support
From our client interactions, being able to smoothly onboard the different content sets is critical to unlocking value in a credit memo. The S&P Global Market Intelligence Solutions Architect team works with clients to help design an end-to-end data solution. This team also partners with our clients to embark on the journey from idea generation to implementation. Customers who subscribe to our products and services also have access to our 24/7 Client Support team via phone and email.
Disclosures for this article from S&P Global Market Intelligence: https://www.spglobal.com/marketintelligence/en/legal/disclosures#sp-global-market-intelligence
1 See Firms Improve Efficiencies and Accuracy with Consolidated Delivery of Credit Ratings) for further details on the unified feed of credit ratings.
2 We define this as entities which are not rated by S&P Global Ratings
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