Blog — Apr. 18, 2026
Enhancing Retail Investment Insights for a Regional Securities Firm
THE CLIENT
Regional Securities Firm, Investment Banking
THE USER
Product Owners & Managers
Engagement with a regional securities firm’s AI team began following the 2025 Retail Seminar hosted by the S&P Global Market Intelligence Data & Research sales team. Since then, the firm has shown a strong interest in enhancing its retail investment service through AI-driven insights. Several follow-up meetings were conducted to introduce relevant S&P Global Market Intelligence datasets and explore potential integration opportunities aligned with their roadmap.
Unlike traditional securities firms in Korea, the regional securities firm operates a dedicated team focused on researching and developing proprietary, data-driven investment insights for retail users. In the US equity market, where investment in mega-cap technology stocks is already highly saturated, the firm was seeking to help users identify next-generation investment opportunities by systematically discovering related companies through company networks and relationship data.
To support this objective, multiple datasets were introduced, including Business Relationships, Business Relationships Analytics, Company Relationships, Company Intelligence, Visible Alpha, and Key Developments. Following several months of detailed data sample reviews and trial evaluations, the firm confirmed its decision to proceed with the subscription
Value Proposition and Use Cases
The partnership aims to enable the firm's investment service to surface S&P Global Market Intelligence’s company relationship context and generate AI-supported rationales by combining multiple datasets, enhancing the quality, depth, and explainability of retail investment insights.
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Contextual Display: Display permitted company relationship and reference data within the client’s investment platform to provide users with relevant context on company connections. |
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AI-Supported Derived Insights: Use S&P Global Market Intelligence datasets as inputs to generate derived analytical outputs, including summaries, explanatory rationales, and AI Signal content, to help users understand stock movements and identify related companies based on real-world business relationships. |
Cost of the Problem and Performance Value
For the regional securities firm, which aims to differentiate itself through innovative, data-driven content, the existing method for identifying related stocks relied primarily on news-based associations. This presented clear limitations in terms of depth, reliability, and scalability.
By subscribing to and integrating our company network and qualitative datasets, the regional securities firm can:
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Improve the interpretability and credibility of AI-generated investment signals by grounding network-based explanations in real-world corporate relationships. |
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Increase user engagement by enabling users to discover related stocks based on structured business and corporate relationship data. |
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Enhance thematic discovery features, particularly by leveraging Topic Tags to allow users to identify stocks aligned with their specific areas of interest. |
Competitive Dynamics
The regional securities firm currently uses a competitor for fundamental datasets. Their use case required not only global market coverage but also high-quality coverage of Korean companies, making data coverage quality and depth a key decision factor.
While similar relationship information could be accessed through a financial data and analytics platform, licensing and redistribution restrictions posed significant limitations for productization within a retail platform. Additionally, S&P Global Market Intelligence’s datasets were evaluated as superior for content structure, ease of use, and suitability for integration into retail investment services.
Other Factors
Due to the inherent complexity of company relationship datasets, the review process presented several challenges, including interpreting data structures and validating certain relationship linkages. However, proactive, hands-on support from the Solutions Architect team played a critical role in addressing technical questions and ensuring a successful implementation.
Replicating Success
This engagement represents a strong reference case for other securities firms seeking to develop differentiated, AI-driven retail investment content. It positions Company Network data not as raw data delivery, but as an explanatory layer that enables platforms to answer “why” and “how companies are connected,” thereby enhancing the interpretability and value of AI-generated insights.
Additionally, the regional securities firm is expected to continue expanding its AI-driven content capabilities, creating future opportunities to introduce additional datasets, including Alpha Signals' specialty models, for retail-oriented use cases.
This article was published by S&P Global Market Intelligence and not by S&P Global Ratings, which is a separately managed division of S&P Global.