Production GenAI in Payments: 3 Systems and 1 Banking Pilot

Ran GenAI use-case identification across the organisation, evaluated 20+ candidates with value modeling (ROI / feasibility / data readiness), and shipped three production systems plus one banking pilot. Built the governance posture so AI runs alongside PCI DSS, ISO 27001 and AML/CFT controls, not despite them.

The organisation had more AI ideas than production candidates. The useful filter was not model novelty; it was whether a use case had clean source data, a measurable operating cost, a human fallback, and an audit trail a regulator or bank partner could understand.

• AI Merchant Integration Chatbot (Slack + Telegram), RAG over API docs, error catalogue and integration playbooks. Cuts merchant integration support time by 65%.
• Intelligent System Monitoring & Auto-Escalation Bot, detects payment error spikes, runs log analysis, identifies root cause, auto-escalates with full diagnostics.
• AI Partner Support Automation, handles 90% of merchant payment queries (settlement, disputes, decline codes, integration) without human intervention.
• Fraud Detection & AML Pilot, active pilot with a major banking partner; value model projects 40% reduction in manual review queues.
• Use-case identification + value-modeling framework, ROI, feasibility, data readiness, regulatory risk per candidate.

• RAG-first for any LLM surface that touches merchant/partner instructions, citations always shown
• Domain-specific embedding index per use case (integration docs, decline codes, dispute taxonomy)
• Audit trail for every AI decision (input, retrieved context, model output, human override)
• Feature store + analyst-feedback loop for the fraud/AML pilot
• Open-source LLMs for non-sensitive surfaces, vendor LLMs for narrower use cases

• AI use cases reviewed in a monthly product + risk + compliance council
• Every AI surface ships with a kill-switch and a human-in-the-loop fallback
• Quarterly bias / drift / hallucination audit against held-out cases

Led GenAI strategy end-to-end at a cross-border payments platform, use-case identification, value modeling, vendor selection, regulator briefings, build/buy decisions, deployment governance and post-launch measurement.

• 3 production AI systems live; 1 banking pilot in flight
• Merchant integration support time −65%, automated incident escalation, 90% partner queries auto-resolved
• Established the AI use-case discipline (ROI / feasibility / data readiness / regulatory risk) used quarterly
• Briefed regulators on the platform's AI posture for licence reviews

• Chose RAG + open-source LLMs over closed APIs for the most sensitive merchant-facing surfaces, slower iteration, lower data-egress risk
• Built our own value-modeling framework rather than adopting a vendor scorecard, better fit, more upfront effort

• Most AI value in payments today is in operations and integration support, not in the customer-facing UI.
• Auditable behaviour beats raw model performance. A 92% model with full citations is better than a 96% model that can't explain itself.
• The fraud/AML use case has the highest stated ROI and the longest validation timeline. Plan for that.

The transferable core: getting four use cases through value modelling, data-readiness review, risk sign-off, production fallback design and post-launch measurement, not the fact that AI was used. Every network, PSP and cross-border platform needs exactly that discipline; only the logo changes.