Erosion risks

• Bloomberg remains the dominant terminal in many workflows
• AI-native tooling could change how workflows are delivered (less dependence on traditional terminals)
• Aggressive price competition in enterprise data feeds

Leading indicators

• Annual Subscription Value growth (Data & Analytics)
• Net retention / churn for Workspace
• Enterprise feed win/loss rates

Counterarguments

• Many large institutions multi-source data and can switch modules over time
• Bloomberg ecosystem integration can outweigh LSEG's workflow advantages in front-office use cases

Erosion risks

• Commoditization of some datasets via open data and cheaper vendors
• Regulatory scrutiny of market data pricing could cap monetization

Leading indicators

• Gross margin trends in Data & Analytics
• Unit pricing trends for data feeds
• Content acquisition (third-party data) cost inflation

Counterarguments

• Scale advantages can be competed away if content costs rise and customers demand price reductions
• Differentiation may be more product/UX-driven than cost-driven

Erosion risks

• Fee compression as large asset managers negotiate lower index licensing fees
• Growth of self-indexing / custom indices by large asset managers
• Benchmark regulation changes increasing compliance costs (e.g., benchmark administrator rules)

Leading indicators

• Index licensing revenue growth
• ETF AUM tracking FTSE Russell indices
• Net new index mandates (wins/losses)

Counterarguments

• Switching costs can be low for new products; mandates can choose MSCI/S&P alternatives
• Large clients can diversify index providers and reduce dependency on any single benchmark family

Erosion risks

• Index methodology controversies can damage reputation
• Regulatory enforcement or benchmark restatements

Leading indicators

• Benchmark governance incidents
• Client concentration changes
• Renewal rates for index licenses

Counterarguments

• Brand trust is shared by several incumbents (MSCI, S&P Dow Jones), limiting differentiation

Erosion risks

• Increasing use of alternative/open-source data and AI entity resolution
• Regulatory changes requiring different screening approaches
• Competitive offerings with comparable datasets and lower pricing

Leading indicators

• Customer renewal rates in Risk Intelligence
• Time-to-update for sanctions/PEP datasets
• False-positive and true-positive screening performance metrics (where disclosed)

Counterarguments

• Large banks often use multiple vendors and in-house models, limiting lock-in
• Data quality differences can narrow over time as competitors invest

Erosion risks

• Standardization of APIs and data formats reduces integration friction
• Procurement shifts toward best-of-breed point solutions

Leading indicators

• Net revenue retention for Risk Intelligence
• Expansion into adjacent risk modules per customer

Counterarguments

• Compliance tooling can be modular and swapped without a full platform replacement

Erosion risks

• Fragmentation of trading across alternative venues and internalization
• Lower IPO activity and shift to private markets reduces capital formation volumes
• Regulatory changes affecting market structure and fee models

Leading indicators

• Equity capital raised and IPO pipeline trends
• Market share of on-book trading vs off-book/MTFs
• Tradeweb and FX platform volume trends

Counterarguments

• Liquidity is portable and can shift to alternative venues when pricing/latency is superior
• Global issuers can choose US/EU exchanges if listing economics are better

Erosion risks

• Market outages or integrity incidents can damage reputation
• High-profile regulatory enforcement actions

Leading indicators

• Operational resilience metrics and outage history
• Issuer retention and new listings
• Regulatory findings and remediation actions

Counterarguments

• Brand trust is shared among major incumbent exchanges, limiting differentiation

Erosion risks

• Regulatory pressure to relocate or split clearing by jurisdiction (e.g., EU active account requirements)
• Competitive incentives from rival CCPs (e.g., Eurex, CME)
• Tail-risk events (member default) could harm trust and prompt regulatory intervention

Leading indicators

• Share of cleared OIS/IRS volumes vs major CCP competitors
• Client trade counts and number of active clearing members
• Regulatory decisions affecting CCP recognition and location policy

Counterarguments

• Regulators can mandate clearing location, weakening network effects by force
• Large dealers can support multiple CCPs if economics/regulation shift

Erosion risks

• Regulatory changes to CCP requirements increase compliance costs
• Political pressure to diversify away from a single dominant CCP in key products

Leading indicators

• Changes in CCP recognition regimes (UK/EU/US)
• Material supervisory findings or enforcement actions
• Capital and default-fund requirement changes

Counterarguments

• Regulatory approval is a barrier, but incumbents still face competition once multiple CCPs are recognized