Global Risk Modeling Platforms and Financial Dependency

Introduction

Global risk modeling platforms are technology systems that collect, process, and model financial, economic, insurance, credit, market, climate, catastrophe, and operational data to estimate risk. They may provide credit-risk scores, stress-testing models, Value-at-Risk calculations, catastrophe probabilities, default predictions, portfolio analytics, liquidity assessments, or systemic-risk indicators.

Examples include risk-data providers, credit-rating and scoring infrastructures, financial-data terminals, insurance catastrophe-model providers, cloud-based risk engines, and AI-driven financial analytics platforms.

The competition-law concern arises when banks, insurers, asset managers, pension funds, clearing institutions, or regulators become economically or technologically dependent upon a small number of risk-modeling providers. Dependency can create:

• barriers to entry;
• switching costs;
• data advantages;
• interoperability problems;
• algorithmic lock-in;
• discriminatory access;
• excessive pricing;
• exclusionary licensing;
• vertical foreclosure;
• coordinated reliance on common models; and
• systemic financial vulnerability.

The issue therefore lies at the intersection of competition law, financial regulation, data governance, digital-platform economics, and systemic-risk regulation.

1. Meaning of Financial Dependency

Financial dependency exists when a financial institution cannot realistically replace a particular risk-modeling platform without substantial cost, delay, regulatory difficulty, loss of historical data, or deterioration in risk-management capability.

Dependency may arise from five principal sources.

1.1 Data dependency

A platform may possess enormous historical datasets concerning:

• defaults;
• securities;
• insurance claims;
• market prices;
• counterparties;
• corporate financials;
• catastrophe events;
• trading behavior.

A rival may therefore face significant difficulty reproducing the incumbent's informational advantage.

1.2 Model dependency

Financial institutions may integrate a provider's model directly into:

• underwriting;
• capital allocation;
• portfolio management;
• pricing;
• collateral requirements;
• stress testing;
• regulatory reporting.

Once embedded, replacing the model becomes costly.

1.3 Regulatory dependency

A particularly important form of dependency arises where regulators or regulated institutions accept particular methodologies, ratings, models, or validation frameworks.

If regulatory compliance effectively requires use of a particular methodology, the provider may acquire a form of regulatory-enabled market power.

1.4 Technological dependency

APIs, proprietary formats, cloud infrastructure, historical databases, dashboards, and software integrations can create switching barriers.

1.5 Network dependency

Where many institutions use the same risk infrastructure, that platform may become more valuable because:

everyone else uses the same system.

This can produce self-reinforcing concentration.

2. Relevant Competition-Law Markets

The relevant market does not necessarily consist of "financial software" generally.

Potential markets include:

1. credit-risk modelling;
2. catastrophe-risk modelling;
3. financial-market data;
4. enterprise risk-management software;
5. regulatory risk analytics;
6. portfolio-risk analytics;
7. credit-rating information;
8. benchmark and index services;
9. liquidity-risk analytics;
10. stress-testing platforms;
11. cloud-hosted risk-management infrastructure;
12. AI-driven financial forecasting.

Market definition should consider functionality, substitutability, data quality, regulatory recognition, interoperability, geographic scope, and switching costs.

3. Why Risk Models Can Become Essential Inputs

A risk model can resemble an essential input even when it is not technically indispensable.

Suppose 90% of major financial institutions use one model because:

• it has decades of historical data;
• regulators understand its methodology;
• auditors are familiar with it;
• counterparties accept its outputs;
• internal systems are integrated with it;
• alternative models have insufficient validation histories.

The platform can consequently become a de facto infrastructure layer.

Competition authorities should therefore distinguish:

technical substitutability
from
economically realistic substitutability.

A rival model may technically exist but remain commercially incapable of disciplining the incumbent.

4. Competition Risks

A. Excessive pricing

A dominant platform may increase:

• subscription charges;
• API fees;
• model-licensing fees;
• data-access charges;
• customization costs.

The more deeply institutions are integrated into the system, the less sensitive they become to price increases.

B. Data foreclosure

The incumbent may restrict access to:

• historical datasets;
• model outputs;
• APIs;
• metadata;
• model documentation;
• calibration information.

This can prevent competitors from developing credible alternatives.

C. Interoperability restrictions

A platform could make it difficult to export:

• historical risk calculations;
• portfolio data;
• model parameters;
• scenario libraries;
• audit trails.

This creates technical switching costs.

D. Bundling

A dominant provider might bundle risk models with:

• financial data;
• trading platforms;
• analytics;
• cloud services;
• ratings;
• compliance software.

Customers may effectively be forced to purchase an entire ecosystem.

E. Vertical foreclosure

A company operating both upstream data infrastructure and downstream financial services could potentially disadvantage competing financial institutions.

5. Algorithmic Herding

One of the most important modern risks is common-model dependency.

If hundreds of banks use substantially similar models, their decisions may become correlated.

For example:

Bank A → Model X → reduces lending
Bank B → Model X → reduces lending
Bank C → Model X → reduces lending

The individual decisions may be rational, but collectively they can amplify systemic volatility.

This creates an important distinction:

Competition risk

The platform excludes competing model providers.

Systemic-risk concern

The entire financial system becomes dependent on the same assumptions.

Thus, competition law can intersect with financial-stability regulation.

6. Credit Ratings and Regulatory Dependency

Credit-rating systems provide a particularly useful historical analogy.

Where legislation or market practice gives recognized ratings importance in:

• capital requirements;
• investment mandates;
• collateral rules;
• insurance regulation;

rating agencies can acquire significant structural importance.

The resulting concern is not simply whether the rating is accurate.

The deeper issue is:

Does regulatory architecture cause market participants to converge on a small number of private information providers?

That question remains highly relevant to AI-driven risk models.

7. Six Important Case Laws

1. United Brands Company v Commission — 1978

The European Court of Justice established important principles concerning dominance and economic dependence.

United Brands concerned the banana market, but its broader significance lies in recognizing that dominance involves the ability of an undertaking to behave to an appreciable extent independently of competitors, customers, and consumers.

Relevance

A risk-modeling platform could potentially possess dominance where customers cannot effectively discipline it because switching requires:

• rebuilding databases;
• revalidating models;
• obtaining regulatory approval;
• rewriting APIs;
• retraining personnel.

The case therefore provides a conceptual foundation for examining economic dependence and market power.

2. Bronner v Mediaprint — 1998

The ECJ established the demanding conditions for applying the essential-facilities doctrine to a refusal to supply.

Generally, an undertaking cannot be compelled to provide access to its infrastructure merely because the infrastructure would make competition easier.

The Court emphasized factors including indispensability and the elimination of effective competition.

Relevance to risk platforms

Suppose a dominant risk-data provider refuses access to historical datasets or model infrastructure.

A competition authority would need to examine whether:

• the information is genuinely indispensable;
• competitors can realistically reproduce it;
• duplication is economically feasible;
• refusal eliminates effective competition; and
• there is no objective justification.

This makes Bronner highly relevant to data and model-access disputes.

3. IMS Health GmbH & Co OHG v NDC Health — 2004

IMS Health is one of the leading EU cases concerning access to commercially valuable information infrastructure.

The dispute involved pharmaceutical sales data organized according to a proprietary structure.

The Court applied the exceptional circumstances doctrine concerning refusal to license intellectual property.

Relevance

Risk-modeling platforms frequently rely upon proprietary:

• databases;
• classifications;
• risk taxonomies;
• data structures;
• analytical methodologies.

IMS Health demonstrates that intellectual-property protection does not automatically prevent competition-law scrutiny where proprietary infrastructure becomes indispensable for effective competition.

4. Microsoft Corp. v Commission — 2007

The EU Microsoft litigation is central to understanding interoperability and technological foreclosure.

Microsoft was found to have abused its dominant position through conduct involving interoperability information.

Relevance to risk modeling

A dominant risk platform could theoretically create similar competitive problems if it prevents competitors from interoperating with:

• banking systems;
• trading infrastructure;
• cloud platforms;
• regulatory reporting systems;
• portfolio-management software.

The key lesson is that interoperability can itself be a competitive parameter.

5. Slovak Telekom a.s. v Commission — 2021

The EU litigation concerning Slovak Telekom is important for understanding exclusionary conduct involving infrastructure access.

The case reinforces the principle that competition law can scrutinize conduct by a dominant undertaking controlling an important infrastructure where access conditions disadvantage competitors.

Relevance

A dominant financial-risk platform could potentially engage in exclusionary conduct through:

• discriminatory API access;
• unfavorable licensing;
• technical degradation;
• discriminatory data formats;
• unequal access to critical functionality.

The precise legal test depends upon the conduct and circumstances, but the case is useful for analysing infrastructure-based foreclosure.

6. Google Shopping — Google and Alphabet v Commission — 2024

The Google Shopping litigation is highly relevant to modern digital-platform competition.

The European courts upheld the Commission's finding concerning Google's use of its dominant position in general search to favor its comparison-shopping service.

Relevance

The principle is broader than search engines.

A dominant risk-data ecosystem could potentially favor its own downstream:

• risk products;
• investment analytics;
• insurance models;
• credit products;
• financial services.

The competitive concern becomes particularly strong where the same platform controls both:

the information layer
and
the downstream service layer.

8. Additional Important Authorities

Several additional cases provide useful analytical support.

Michelin v Commission — 1983

Important for understanding how dominance can affect customer behavior through commercial conditions and dependence.

Risk-platform relevance: long-term contracts and loyalty mechanisms may make financial institutions increasingly dependent upon a single provider.

Hoffmann-La Roche v Commission — 1979

Important authority on exclusionary conduct and loyalty arrangements.

Risk-platform relevance: exclusivity arrangements involving risk databases or analytical systems could potentially foreclose rival providers.

Magill — 1991

Important intellectual-property/refusal-to-license authority.

Risk-platform relevance: proprietary risk information may raise difficult questions concerning access to data necessary for downstream competition.

MEO v Autoridade da Concorrência — 2018

Relevant to discriminatory pricing and competitive disadvantage.

Risk-platform relevance: differential licensing or API pricing between financial institutions may warrant examination where competitive harm is established.

9. United States Perspective

US antitrust law provides complementary tools.

Relevant doctrines include:

• Sherman Act §1;
• Sherman Act §2;
• Clayton Act §7;
• tying;
• exclusive dealing;
• monopolization;
• attempted monopolization;
• essential-input theories in limited circumstances;
• merger control.

The US approach generally requires careful demonstration of anticompetitive effects, rather than treating mere dependence upon a successful technology as unlawful.

This is especially important for risk models because superior accuracy, better data, or lower costs can legitimately produce substantial market share.

10. Merger-Control Dimension

Risk-modeling markets can become particularly problematic through acquisitions.

Consider:

Risk-model provider + financial-data provider
↓
proprietary data advantage
↓
better model
↓
more customers
↓
more transaction data
↓
even better model

This creates a data-feedback loop.

A merger may therefore create competitive concerns even where the parties have relatively modest traditional revenues.

Authorities may investigate:

• data aggregation;
• vertical foreclosure;
• access to model inputs;
• interoperability;
• downstream financial services;
• innovation competition;
• entry barriers.

11. AI Risk Models

AI substantially changes the problem.

Traditional models can often be documented through relatively transparent formulas.

AI systems may depend upon:

• massive training datasets;
• proprietary embeddings;
• machine-learning architectures;
• continuously changing parameters;
• opaque feature engineering;
• real-time market data.

Consequently, switching providers may require more than purchasing another software package.

The institution may have to:

1. reconstruct historical datasets;
2. validate a new model;
3. conduct back-testing;
4. demonstrate model stability;
5. obtain governance approval;
6. satisfy auditors;
7. satisfy regulators;
8. retrain employees;
9. integrate APIs;
10. compare outputs over multiple market cycles.

These costs can make model migration itself a competitive barrier.

12. Financial Stability and Competition Law

The most important conceptual issue is that competition policy normally seeks to prevent excessive market power, while financial regulation also seeks to prevent excessive systemic risk.

A highly concentrated risk-model market can produce both.

Scenario

One platform supplies models to:

• banks;
• insurers;
• hedge funds;
• pension funds;
• clearing houses.

A common model suddenly changes its assessment of a particular asset class.

Multiple institutions may respond simultaneously.

The result can be:

common model → common risk assessment → common trading response → correlated selling → liquidity stress.

Thus, concentration can become a systemic-risk multiplier.

13. Regulatory Responses

A comprehensive regulatory framework could include:

13.1 Data portability

Financial institutions should be able to export relevant historical data in interoperable formats.

13.2 API interoperability

Dominant platforms could be required, where legally justified, to provide fair technical interfaces.

13.3 Model transparency

Regulators may require sufficient information regarding:

• methodology;
• validation;
• limitations;
• material model changes.

13.4 Multi-model requirements

Systemically important institutions could maintain alternative models instead of relying entirely upon one provider.

13.5 Exit planning

Large institutions could be required to demonstrate that they can migrate to alternative providers.

13.6 Concentration monitoring

Regulators could monitor market shares in critical financial-data and risk-model infrastructure.

13.7 Auditability

AI models should maintain adequate:

• version histories;
• decision logs;
• validation records;
• documentation.

14. Competition-Law Test

A useful analytical framework is:

Step 1 — Define the market
↓
Step 2 — Identify market power
↓
Step 3 — Measure customer dependency
↓
Step 4 — Examine switching costs
↓
Step 5 — Identify data advantages
↓
Step 6 — Examine interoperability
↓
Step 7 — Determine exclusionary conduct
↓
Step 8 — Assess actual or potential competitive harm
↓
Step 9 — Consider objective justifications
↓
Step 10 — Design proportionate remedies

15. Key Legal Issues

Conclusion

Global risk-modeling platforms can become critical financial infrastructure when banks, insurers, investors and regulators collectively rely upon their data, models and technological interfaces.

The principal competition-law question is not simply whether a provider has a high market share. It is whether data advantages, regulatory recognition, interoperability barriers, contractual lock-in and model-validation costs create durable market power that competitors cannot realistically challenge.

The cases of United Brands, Bronner, IMS Health, Microsoft, Slovak Telekom and Google Shopping provide particularly useful legal frameworks for analysing dominance, indispensability, access to infrastructure, interoperability, exclusionary conduct and leveraging.

The emerging challenge is therefore a dual dependency problem:

financial institutions may depend upon the risk platform, while the financial system itself may become dependent upon the assumptions embedded in that platform.

That makes global risk-modeling markets especially significant for the future of competition law, AI governance, financial regulation and systemic-risk management.