Financial Fact Stability and Assistant Divergence: The Chase Sapphire Assessment

AIVO Case Study

Summary

AIVO conducted a structured evaluation of two leading AI assistants to see how consistently they represented core product information about the Chase Sapphire Preferred credit card. The assistants were given identical prompts covering APR, eligibility rules, bonus structure, and recent changes. The test revealed meaningful inconsistencies, unsupported claims, and differences in factual interpretation. These behaviours illustrate why financial institutions require external verification layers when engaging with AI mediated disclosures.

1. Background and Objective

Credit card terms such as APR, fees, and eligibility criteria are tightly regulated. They must be accurate, consistent, and matched to current market and issuer disclosures. With consumer behaviour shifting toward AI mediated product discovery, financial brands need clarity on how assistants interpret and present their information.

The objective of this assessment was to analyse the stability and alignment of assistant responses when asked straightforward questions about a well-known financial product.

2. Methodology

Testing conducted November 17–20, 2025, approximately five months after Chase's major Sapphire bonus-rule overhaul (June–August 2025).

AIVO tested four prompts across two frontier AI assistants. Each prompt asked for:

• current APR range
• recent interest rate changes
• credit score or risk considerations
• bonus eligibility rules and restrictions
• general application requirements

Prompts were standardised. Responses were analysed using AIVO’s accuracy and variance indicators, including:

• factual integrity
• cross model alignment
• unsupported precision
• fabricated or future dated assertions
• internal consistency within and across responses

3. Key Findings

3.1 Divergence Across Assistants

The assistants provided different answers across almost every core category, including:

• APR ranges
• explanations for APR changes
• eligibility thresholds
• bonus timing rules
• interpretations of issuer policy

This divergence shows that multi model consistency cannot be assumed, even for basic financial facts.

3.2 Unsupported or Over precise APR Values

Both assistants provided narrow APR ranges with precise decimals. Neither disclosed the source of those numbers or the variability that can occur across different offer versions.

For regulated products, this level of unsupported precision represents an accuracy risk.

3.3 Invented or Misapplied Context

One assistant referenced a recent Prime Rate change that had not occurred. This indicates uncertainty blending forecasting patterns with factual history. Financial institutions need to be aware that assistants may introduce contextual elements that diverge from actual market events.

3.4 Conflicting Interpretations of Eligibility Rules

There were inconsistent statements about:

• whether bonus eligibility uses a 48 month or lifetime rule
• whether simultaneous Sapphire cards are permitted
• how underwriting decisions are currently made

These inconsistencies reflect differences in synthesis rather than differences in data. They highlight the need for controlled, verified interpretations of issuer policy when information is mediated through AI assistants.

4. Implications for Financial Institutions

4.1 Regulatory Considerations

Regulations such as TILA, Reg Z, and UDAAP require accurate disclosures of key credit terms. Where assistants provide incorrect or unsupported information, there is potential for:

• consumer confusion
• complaints
• misaligned marketing materials
• compliance risk if institutions rely on assistant outputs

This is particularly relevant as financial brands increasingly use AI tools for customer support and content generation.

4.2 Brand Trust and Customer Experience

Inconsistent or incorrect APR and eligibility information can:

• undermine consumer trust
• increase customer service volumes
• cause confusion during application flows

AI mediated discovery is becoming a primary touchpoint for consumers. Financial brands must maintain accuracy across all channels where their products are interpreted or summarised.

4.3 Need for Verification and Monitoring

This test demonstrates that even simple, well defined financial products can be represented differently across assistants. Financial institutions require:

• independent verification
• factual integrity checks
• cross model reconciliation
• drift tracking over time

These controls ensure that assistant mediated disclosures remain accurate, aligned, and compliant.

5. How AIVO Supports Financial Brands

5.1 PSOS Visibility Measurement

AIVO quantifies how consistently brands appear across prompt journeys. In financial services, this helps teams understand how often their product surfaces and how reliably it is represented.

5.2 ASOS Accuracy Screening

AIVO evaluates factual integrity and identifies inaccuracies related to APR, fees, product terms, and eligibility. This provides issuers with a clear view of potential compliance exposure in AI mediated environments.

5.3 Multi Model Reconciliation

AIVO detects and explains differences between assistant outputs. This is essential for institutions working across multiple AI tools, whether internally or in consumer discovery environments.

5.4 Continuous Monitoring

AIVO tracks changes in how assistants interpret financial products over time. This protects brands from silent drift and ensures information remains accurate.

6. Conclusion

The Chase Sapphire assessment highlights a broader pattern: generative assistants vary in how they interpret and present financial product information. The inconsistencies observed here underscore the need for independent verification, especially for regulated products where factual accuracy is critical.

AIVO provides the governance and assurance framework financial institutions require to ensure that AI mediated information remains accurate, consistent, and compliant across assistants and over time.

Note: In mid-2025 Chase officially replaced the 48-month family rule with a once-per-lifetime-per-product restriction and removed the prohibition on holding both Sapphire cards simultaneously. Persistent divergence among assistants on these now-settled terms further illustrates ongoing synthesis and knowledge-update challenges

Contact
To schedule your 30 day control assessment or discuss your external AI control requirements, contact the AIVO team: audit@aivostandard.org