Cross-institution collaboration with a UK banking group and regulators (anonymised)

Authorised payment fraud: designing for speed, signals and supervision

Experimenting with multi-agent fraud detection under tight sprint constraints.

AI & DecisioningEconomic Crime & Fraud

Participated in an APP fraud tech sprint, delivering the "Fastest" award-winning prototype

Coordinated multiple signals and tools through a single orchestration layer

Designed for audited explainability, not just detection performance

Demonstrated potential for reduced time-to-market for fraud controls

Context

Authorised Push Payment fraud is a structurally hard problem: customers approve the payments, attackers move quickly, and regulators are increasing expectations around reimbursement and prevention. The client needed to explore new architectural patterns for fraud detection: multi-agent, multi-signal, and explainable to both internal and external stakeholders.

The Challenge

  • Combine diverse signals: transactional patterns, device data, behavioural features, external intelligence

  • Allow multiple specialised "fraud agents" to run in parallel while maintaining coherent outcomes

  • Ensure that any decision — block, step-up or allow — is explainable post-event

  • Prototype within days, not months, for a regulatory tech sprint setting

Our Approach

Multi-agent orchestration design

Each fraud detection capability (e.g. mule detection, destination risk, behavioural anomaly) modelled as an independent agent. An orchestration layer handled task allocation, result collation and decision aggregation.

Explainability and audit trail

For every payment, the system recorded which agents executed, what they saw, how they voted, and why. Designed to support downstream disputes, reimbursement assessments and regulatory reporting.

Pragmatic implementation

Built using cloud-native components and message-driven patterns. Delivered working prototype within tech-sprint timelines, while still following engineering discipline.

Outcomes

  • Demonstrated that multi-agent architectures can improve fraud signal coverage without losing explainability.

  • Proved that fraud controls can be iterated quickly when backed by a modular, event-driven architecture.

  • Provided the bank with a blueprint for future fraud platform investments and experiments.

Capabilities Deployed

Expertise

Applied AI & Agentic AI, event-driven patterns, risk and fraud domain.

Blueprints

Event Interaction, Runtime Integrity Patterns.

Code Assets

Agent Orchestration Library, Context Security Module.

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