On 1 April, Visa announced six AI-powered tools to automate credit card dispute management for merchants, issuers and acquirers. The problem it addresses is real: disputes have risen 35% since 2019, and manual handling remains costly and slow. For UK financial services firms regulated under PRA SS1/23 and subject to FCA Consumer Duty obligations, this matters because disputes are a direct measure of customer harm. Visa's solution uses generative AI to handle dispute classification, generate merchant responses, and provide order insights. On its surface, this is sensible. The underlying assumption—that large language models can safely manage high-stakes transactional logic without human oversight—is dangerously naive.

This announcement sits at the peak of a broader pattern: major infrastructure players (Visa, Mastercard, clearing houses, settlement networks) are treating generative AI as a cost-reduction hammer. The pattern repeats across financial services: ChatGPT-style tools are applied to high-volume, rule-based work with the promise of speed and scale. The problem is structural. Generative AI excels at prose and pattern-matching. It struggles with deterministic processes where a single error propagates across accounts, regulatory submissions and customer trust. A dispute wrongly classified by an LLM is not a typo—it is a compliance failure under Consumer Rights Act 2015, a potential breach of FCA CASS rules, and grounds for a complaint to the Financial Ombudsman Service. Visa's tools will work for 85% of cases. The remaining 15% will create the most expensive customer service interactions regulators will see.

Trovix's view is direct: generative AI works in dispute management only when it operates inside a deterministic, human-verified gate. Visa's tools appear to automate the entire loop. That is architectural error. Compare this to how Luminance handles document review in legal disputes—it identifies, but humans decide. Or how Harvey manages contract analysis—it suggests, humans confirm. The difference between those approaches and Visa's model is accountability. When Harvey misclassifies a clause, the lawyer is liable. When Visa's LLM mishandles a dispute, who owns the failure? The merchant? The issuer? The cardholder? That ambiguity violates the spirit of FCA Consumer Duty PS22/9, which explicitly requires firms to identify and mitigate harms. Generative AI in dispute management must be transparent, auditable, and human-controlled at decision points. If Visa's tools operate as black-box automation, they will migrate risk rather than reduce it. Use Trovix Sift to handle the document extraction part of dispute management—it is deterministic, explainable, and designed for regulated environments. Use generative AI to accelerate human review, not to replace human judgment.

For mid-market law firms, insurers and financial services practices in the UK: do not copy Visa's approach without structural safeguards. If you are considering generative AI for dispute handling, charge-back management, or claims triage, build in human decision gates at classification, response generation and escalation. Map your process to FCA and PRA expectations for explainability. Document why the AI was used, what it decided, and who verified the outcome. If your vendor cannot show you this audit trail, reject the tool. Visa has the scale to absorb occasional failures. You do not. The competitive advantage is not speed of automation—it is speed of safe, auditable automation. That requires discipline about what generative AI should and should not do. Visa's launch is a warning dressed as innovation.

Source: CNBC