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Navigating the Emerging Risks and Opportunities of Generative AI in the Cross-Border Payment Industry: A Compliance Perspective

A Report by CYS Global Remit Legal & Compliance Office


Part 4: Managing Third-Party and Operational Risks of Generative AI in Cross-Border Payments


Introduction

As financial institutions increasingly integrate generative AI (GenAI) into their compliance workflows, many rely on third-party vendors and cloud-based AI platforms. While these partnerships offer speed and scalability, they also introduce new layers of operational and regulatory risk. For compliance professionals in the cross-border payment space, understanding and managing these risks is essential to maintaining control and trust.


1. The Rise of Third-Party GenAI Providers


  • Many financial institutions lack the in-house capability to build and maintain GenAI models, leading to reliance on external vendors (e.g., OpenAI, Anthropic, Google Cloud AI).

  • These providers often operate across jurisdictions, raising concerns about data sovereignty, model transparency, and contractual accountability.


Compliance Implication: Outsourcing GenAI capabilities does not outsource regulatory responsibility. Institutions remain accountable for how these tools are used and the outcomes they produce. 


2. Key Third-Party Risk Areas


  • Data Privacy & Leakage: Sensitive customer data may be exposed during model training or inference if not properly anonymized or encrypted.

  • Model Opacity: Many GenAI models are “black boxes,” making it difficult to explain or audit their outputs—especially problematic in regulated environments.

  • Service Disruptions: Dependence on external APIs or cloud infrastructure introduces operational risks if services are interrupted or deprecated.

  • Jurisdictional Conflicts: Cross-border data transfers may violate local data protection laws (e.g., GDPR, PDPA, CCPA).


3. Strengthening Operational Resilience


  • Due Diligence: Conduct thorough assessments of GenAI vendors, including their data handling practices, model governance, and regulatory posture.

  • Contractual Safeguards: Include clauses that address:

    • Data ownership and usage rights.

    • Audit and inspection rights.

    • Incident response and breach notification timelines.

  • Redundancy Planning: Avoid single points of failure by maintaining fallback systems or alternative vendors for critical GenAI functions.


4. Governance and Oversight Mechanisms 


  • Third-Party Risk Management (TPRM) frameworks should be updated to include GenAI-specific considerations.

  • Model Risk Management (MRM) should extend to vendor-supplied models, with clear documentation of:

    • Model purpose and limitations.

    • Testing and validation results.

    • Change management protocols.

  • Cross-Functional Collaboration: Compliance, legal, IT, and procurement teams must work together to evaluate and monitor GenAI vendors.


5. Regulatory Expectations


  • Regulators globally are emphasizing accountability and transparency in AI outsourcing arrangements.

  • Institutions are expected to:

    • Maintain clear oversight of third-party GenAI tools.

    • Demonstrate explainability of AI-driven decisions.

    • Ensure data protection compliance across jurisdictions.


Conclusion 

Third-party GenAI tools can accelerate compliance innovation, but they also introduce significant operational and regulatory risks—especially in the cross-border context. Compliance professionals must take a proactive role in vendor governance, ensuring that AI adoption aligns with both internal policies and external expectations. In the final article of this series, we’ll explore how compliance teams can build future-ready capabilities to thrive in the GenAI era. 

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