[Narrator]

Hello everyone, welcome back to the TokenMinds Training series.
Today we’ll walk through how banks can build AI payment layers on top of existing rails to enable real-time verification, controlled execution, and immutable audit trails, without replacing core systems.

This session is about practical architecture.
We’ll cover how AI payment layers integrate into existing banking infrastructure, how they remove execution friction at scale, and how banks can preserve regulatory control while upgrading decision-making to real time.

Payment volumes are increasing, and fraud is evolving rapidly.
However, most banks still rely on static rules and manual reviews.
This leads to legitimate customers being declined, fraud adapting faster than controls, and exception handling growing linearly with volume.
Payments continue to execute, but they do not think or adapt.
The real gap is real-time decisioning across the entire payment lifecycle.

Client Payment Buddy is a leading fintech in Pakistan enabling digital invoice issuance and multi-channel payment collection.
They handle payment presentment through invoices and links, collect funds across cards, bank transfers, wallets, and over-the-counter channels, and reconcile payments automatically to merchant ledgers.
As volume scaled, manual controls and reconciliation became a bottleneck.

Every inbound payment enters the system and is evaluated by AI in real time.
Behavioral signals, transaction history, and risk patterns are scored instantly.
Low-risk payments are automatically approved with no delay.
High-risk payments are flagged and routed to compliance teams for review.
This reduces fraud exposure while improving customer experience by removing unnecessary declines.

AI replaces manual matching across the entire payment flow.
Invoices are matched to payments automatically, even across partial or multiple transactions.
Ledger mismatches are detected instantly instead of days later.
Audit trails are generated continuously, not at month-end.
As a result, reconciliation errors dropped sharply, manual headcount was reduced, and the system became audit-ready at all times.

Settlement is no longer fixed or static.
AI adjusts settlement timing based on real-time risk and context.
Low-risk merchants receive faster payouts.
Settlement logic adapts to weekends, weekdays, liquidity conditions, and merchant behavior.
Funds are automatically split between merchant payouts, reserves, and fees, creating dynamic and controlled execution.

First, real-time risk intelligence evaluates behavioral and transaction signals before funds move.
Second, adaptive decision execution adjusts approvals and settlement dynamically instead of relying on fixed rules.
Third, continuous learning allows models to improve with every transaction and adapt to new fraud patterns automatically.
The result is fewer false declines, lower fraud losses, and scalable operations.

AI payment layers connect through API-first integration using webhooks and REST APIs.
Existing ledgers, compliance providers, and payment rails remain in place.
Wallet risk scoring, sanctions screening, and AML checks sync in real time.
Reconciliation engines support partial payments and deterministic reporting.
Settlement and liquidity logic integrate directly with treasury and FX systems for faster settlement cycles.

TokenMinds delivers end-to-end infrastructure combining AI and blockchain for payment modernization.
We have live deployments with a major bank in Mongolia and Payment Buddy in Pakistan.
Our work is built around three core capabilities: AI payment monitoring and automation, agentic payment systems with policy controls, and integration layers for legacy systems.
The result is modernization without disruption to existing rails.

Thank you for watching and see you in the next training video.