Business case and benefits
- Head of fraud
- A · Accountable
- Product owner
- R · Responsible
- Solutions architect
- C · Consulted
- Technical PM
- C · Consulted
- Engineers
- I · Informed
- Model risk
- C · Consulted
- Compliance & DPO
- C · Consulted
- Security
- I · Informed
Risky payments scored in milliseconds, only the highest-risk band held automatically under rules a person approved, every other alert reviewed by an analyst before any hold, and people making every decision about a customer's payment or claim.
At a glance
An illustrative scenario. The numbers are typical examples, not a client case study. The cloud services and UK rules were checked on 9 November 2026.
An authorised push payment (APP) scam is a payment the customer makes themselves. They have been persuaded by someone posing as their bank, a builder, a romantic partner or an investment firm. The login is genuine, the device is theirs and the payment is authorised, so the controls built for stolen cards and hijacked accounts see nothing wrong. UK Finance reported £576.4 million lost to APP fraud in 2025 across 248,070 cases. Two thirds of those cases started online and about a sixth over the phone, long before the payment reached the bank.
Since 7 October 2024, the sending and receiving firms share the cost of reimbursing victims of APP scams over Faster Payments, up to £85,000 per claim, with matching rules for CHAPS. The reimbursement decision is due within five business days. That makes fraud a balance-sheet question as well as a customer one. Hold too few payments and the losses grow. Hold too many and good customers are delayed, the contact centre fills up, and the firm risks failing the Consumer Duty in the other direction.
In a typical challenger bank, the fraud engine produces a score and a list of rule hits. An analyst then spends ten or fifteen minutes per alert piecing together the payee's history, the Confirmation of Payee result, recent logins and what the customer said in chat, before phoning the customer. Claims handlers rebuild the same picture weeks later, by hand, to decide whether the customer is reimbursed. The knowledge is there. It just isn't in one place when the decision is made.
Agree these, and measure the baseline, before anything is built. Example targets are typical, not promises.
Value lost per million outbound payments, split by scam type and by where the scam started.
ExampleDown by a third against last year
The share of alerts and holds that turn out to be scams or need intervention, with the automatic-hold band measured on its own. Low precision means good customers are being stopped.
ExampleDoubled, from a low base
Genuine payments held, and how long each one waited. This is the Consumer Duty side of the scale.
ExampleFewer, and most released within the same day
Time from opening an alert to a decision, including the customer call.
ExampleDown by half
Reimbursement decisions made within five business days, or within 35 when the clock is stopped for good reason.
ExampleEvery claim, every month
How often analysts disagree with the model or the summary. Very high means it isn't helping; near zero can mean people have stopped checking.
ExampleStable, and reviewed weekly
Cross-sector rules are on the UK regulation map.
Victims of APP scams over Faster Payments are reimbursed up to £85,000 per claim, split equally between the sending and receiving firms, with matching rules from the Bank of England for CHAPS. A firm may apply an excess of up to £100, but not to vulnerable customers. Claims can be made up to 13 months after the payment. Decisions are due within five business days, or 35 if the clock is stopped. The AI gathers the evidence and tracks the deadlines. A person decides every claim, and every finding of gross negligence, which is a high bar and doesn't apply to vulnerable customers.
A firm may delay an outbound UK sterling push payment where it has reasonable grounds to suspect fraud or dishonesty, established by the end of the next business day. The delay must be no longer than necessary, and never beyond the fourth business day. The customer must be told about the delay, the reason and what they need to do, unless telling them would be unlawful. The firm is liable for interest and charges the delay causes, even if the payment is then sent. So only the highest-risk band is held automatically, under rules a person approved: the notice goes out automatically, and an analyst decides to release or refuse. Every other alert goes to an analyst before any hold, and only the analyst can apply one.
Name checking is now permanent: the PSR removed the direction's expiry date in October 2026. The CoP result (match, close match, no match) is a strong signal for the model, and the warning the customer saw is key evidence in any claim, so both are stored with the payment.
There's no AI-specific rulebook. The FCA applies the Duty to AI like any other change. Firms must avoid foreseeable harm, which cuts both ways: scams that should have been caught, and genuine payments blocked without good reason. Outcomes, including hold rates and release times, are monitored by customer group, including vulnerable customers.
Section 188 (in force since 15 January 2024) lets regulated firms share customer information with each other to prevent economic crime, without breach of confidence claims. It doesn't remove UK GDPR duties or the tipping-off offence. The failure to prevent fraud offence (in force since 1 September 2025) covers fraud by staff or agents intended to benefit the firm, so claims handling needs reasonable procedures too: no misleading a customer to avoid paying a valid claim.
Only the temporary hold on the highest-risk band is automatic, because it has to happen in milliseconds, and it follows rules a person approved. Every other hold is an analyst's decision. Since 5 February 2026, decisions like this need safeguards rather than a ban, unless special category data is used: telling the customer, letting them respond and contest it, and a person who can step in. The delay notice and the analyst review provide exactly that. Keep special category data, such as health details from a vulnerability note, out of the scoring model. A DPIA comes before the pilot.
Showing AWSAzuresovereign / on-prem services. Switch with the toggle; every diagram and table on this page follows it.
01Channels
02Intake
03Intelligence
04Orchestration
05Systems of record
06People
| Layer | AWS | Azure | Sovereign / on-prem |
|---|---|---|---|
| Scoring API and payment eventsThe payments hub calls the scoring API and waits a fixed few milliseconds. If no answer comes back, it falls back to your existing rules. It never waits on the AI. | API Gateway, Amazon MSK or Kinesis Data Streams | API Management, Azure Event Hubs | Kong or your existing API gateway; Apache Kafka |
| Real-time scoring modelA gradient-boosted model with reason codes, not a language model. Amazon Fraud Detector closed to new customers on 7 November 2025, so new AWS builds use SageMaker AI or keep the fraud engine you already have. | Amazon SageMaker AI real-time endpoint | Azure Machine Learning managed online endpoint | MLflow and Kubeflow on Kubernetes |
| Features (online)Payee age, CoP result, new device, first payment to this payee, recent password reset. Calculated the same way for training and for live scoring. | SageMaker Feature Store | Azure Machine Learning managed feature store | Feast with Redis, on Kubernetes |
| Mule and network analysisLinks receiving accounts that share devices, payers or rapid pass-through patterns. Microsoft points heavier graph analytics to Graph in Microsoft Fabric. | Amazon Neptune | Azure Cosmos DB for Apache Gremlin | JanusGraph, or another open-source graph database, on Kubernetes |
| Summaries and draftsUsed after an alert is raised, never to decide whether to hold a payment. Both clouds offer a choice of models, including Claude. | Amazon Bedrock | Microsoft Foundry | Open-weight models served with vLLM on your own GPUs, or IONOS AI Model Hub |
| Scam patterns and policy (retrieval)Your own scam typology library, call scripts, reimbursement policy and the regulator's guidance, versioned and owned by the fraud strategy team. | Bedrock Knowledge Bases | Foundry IQ, built on Azure AI Search | OpenSearch, or PostgreSQL with pgvector |
| Agent runtimeThe original Bedrock Agents closed to new customers on 30 July 2026; new builds use AgentCore. | Amazon Bedrock AgentCore | Foundry Agent Service | An open-source agent framework (e.g. LangGraph) on Kubernetes |
| WorkflowTracks the delay clock to the end of the fourth business day, and the claim clock to five or 35 business days. | AWS Step Functions | Logic Apps or Durable Functions | Temporal, or n8n |
| Link to the payments hub and core bankingRelease, refuse and freeze actions stay in the payments hub, behind its own permissions. | API Gateway, plus your existing integration platform | API Management, plus your existing integration platform | Kong or your existing API gateway |
| Human reviewAnalysts and claims handlers work where they already work, not in a new screen. | In the analysts' existing fraud case management tool | In the analysts' existing fraud case management tool | In the analysts' existing fraud case management tool |
| GuardrailsBlocks drafts that accuse the customer, promise an outcome, or mention a suspicious activity report. | Bedrock Guardrails | Foundry guardrails (Azure AI Content Safety) | Open-source guardrails (e.g. NeMo Guardrails, Llama Guard) |
| Identity, keys, data protection | IAM Identity Center, KMS, Macie | Entra ID, Key Vault, Purview | Keycloak or your existing Active Directory; OpenBao or HashiCorp Vault; an HSM |
| Monitoring and model riskSageMaker Model Monitor and Clarify closed to new customers on 30 July 2026. Log every score, reason code, hold, release and claim decision, with who made it. | CloudWatch, CloudTrail, plus drift and fairness checks in your own pipeline | Azure Monitor, Log Analytics, Azure Machine Learning model monitoring | Prometheus, Grafana, OpenTelemetry, with drift checks in MLflow |
| UK data residencyIONOS AI Model Hub runs in German data centres: EU, not UK, residency. | eu-west-2 (London) | UK South / UK West | Your own UK data centre, or a UK or EU provider |
On a phone, the table shows the platform you pick. Sovereign / on-prem means open, portable components you can run in your own data centre or with a UK or European provider; IONOS is shown as one example, and others exist. What sovereign cloud means →
Many organisations keep core systems and sensitive data on their own servers. This is how the same design works across both.
Stays on-prem
The payments hub, core banking and the Faster Payments connection stay where they are, often in your own data centre. Real-time scoring sits right next to them, on-prem or in the same cloud region, because a network round trip of tens of milliseconds is too slow.
Uses cloud AI
Alert summaries, the claims assistant, retrieval over scam patterns and model training can run in a UK cloud region, or on your own GPUs with open-weight models if customer data must stay inside your estate.
How they connect
A private link (AWS Direct Connect or Azure ExpressRoute) and your API layer. Only the fields an alert or claim needs are sent, and results come back as events onto your queue.
If the link drops
Scoring and the approved hold rules keep running locally, so payments are still checked, the highest-risk band is still held, and the delay notices still go out. Analysts work alerts with the score and rule hits, as before, and summaries catch up when the link returns.
What each agent may do through its tools, what it may not, and where a person signs off. The limits are enforced by the platform, not the prompt.
Alert investigation agent
Where a person signs offA fraud analyst decides every hold outside the approved highest-risk band, every release and every refusal, and owns the customer conversation. The agent's permissions are enforced by the platform's identity and policy controls, not by instructions in a prompt.
Claims assistant
Where a person signs offA claims handler decides every claim. Declines and gross negligence findings get a second review before the customer is told.
The scoring call is synchronous and has a strict time budget, because Faster Payments are near-instant. The payments hub calls the scoring API, waits a few milliseconds, and applies your existing rules if nothing comes back. The model is an extra signal on the decision path, never a single point of failure.
Everything after the score is asynchronous. An alert or an automatic hold becomes an event on a queue, and the summary, the evidence and, for a hold, the customer notice follow from it. If the language model is slow or down, analysts see the score and the rule hits as they do today, and the summary arrives late. Every event carries the payment ID, so a retry never creates a second alert or a second notice.
Data shared with other firms, whether under section 188, through a fraud data-sharing body or through the claims system, goes through one controlled interface with its own log. It never goes straight from the agent.
Map today's alert and claims process, measure the baseline, check the data the payments hub can send in time, and agree the scam types to start with.
Gate · must be true to passBaseline agreed, latency budget confirmed with payments engineering, DPIA started, model risk and compliance involved.
Shadow mode: the model scores every payment alongside the existing engine, summaries are produced for real alerts, and analysts compare them with their own work.
Gate · must be true to passBetter precision at the same or lower losses, no customer group worse off, summaries rated useful by analysts, and model risk sign-off.
The model joins the live alert rules for the first scam types, with automatic holds limited to the approved highest-risk band, summaries in the case tool, monitoring and a rollback plan.
Gate · must be true to passMonitoring, support model, rollback switch and analyst training in place.
Add the claims assistant, inbound mule detection, then card and account takeover fraud.
Gate · must be true to passEach new use passes the same shadow-mode gate.
| Activity | Head of fraud | Product owner | Solutions architectI can fill this | Technical PMI can fill this | EngineersI can fill this | Model risk | Compliance & DPO | SecurityI can fill this |
|---|---|---|---|---|---|---|---|---|
| Business case and benefits | Accountable | Responsible | Consulted | Consulted | Informed | Consulted | Consulted | Informed |
| Target architecture and latency budget | Consulted | Consulted | Accountable and Responsible | Consulted | Consulted | Consulted | Informed | Consulted |
| Hold rules and thresholds | Accountable | Responsible | Consulted | Informed | Consulted | Consulted | Consulted | Informed |
| Model validation | Consulted | Consulted | Consulted | Informed | Responsible | Accountable | Consulted | Informed |
| DPIA, Consumer Duty and reimbursement review | Consulted | Consulted | Consulted | Informed | Informed | Consulted | Accountable and Responsible | Consulted |
| Build and integration | Informed | Consulted | Accountable | Responsible | Responsible | Consulted | Informed | Consulted |
| Go-live decision | Accountable | Responsible | Consulted | Responsible | Informed | Consulted | Consulted | Consulted |
| Run, monitor and improve | Accountable | Responsible | Consulted | Consulted | Responsible | Consulted | Consulted | Consulted |
R ResponsibleA AccountableC ConsultedI Informed· not involved
Option A
If there's no architect on the programme, I'd own the design end to end: the scoring path and its latency budget, how the model sits beside your existing fraud engine, the agents, the integration with your payments hub and case tool, and the gates. I'd work closely with your head of fraud and model risk, and report to your programme board.
Option B
If your partner, your fraud engine vendor or your own team already has an architect, I'd take on the agents, the event flows and the payments-hub integration under their lead, and give them a second senior opinion on latency, resilience and the evidence your model risk team will ask for.
Goes wrong
A language model is put in the payment path, and payments slow down or get inconsistent answers.
How the design avoids it
A small, fast scoring model with reason codes raises alerts, and only the approved highest-risk band is held automatically. Generative AI only explains and drafts, after the alert.
Goes wrong
The automatic-hold band quietly widens as thresholds are tuned, and more customers are held without an analyst looking first.
How the design avoids it
Only the fraud strategy owner can change the band, every change is logged, and its size and precision are reported weekly.
Goes wrong
The model learns last year's scams, and the criminals have already moved on.
How the design avoids it
A weekly typology review, fresh labels from claims, a regular retraining cycle, and drift checks owned by a named person.
Goes wrong
Holds go up, losses go down, and good customers are delayed for days.
How the design avoids it
Measure good payments delayed alongside losses, agree a release target, and tune thresholds with the business, not just the data team.
Goes wrong
A drafted message sounds like an accusation, or hints at a suspicious activity report.
How the design avoids it
Approved scripts as the only source, guardrails on every draft, and the analyst sending nothing they haven't read.
Goes wrong
Claims are decided differently by different handlers, and the regulator notices.
How the design avoids it
The same evidence pack and checklist for every claim, a second review of every decline, and a weekly consistency sample.
Changes already announced or under way that affect this design. More in the UK horizon scan.
Keep the claim evidence structured and the checklist configurable, so new guidance changes a rule, not the system. Start recording where each scam began: social media, a marketplace, a phone call.
No redesign is needed. Map each control to the requirement, not the regulator, and watch for FCA consultations on a combined payments rulebook.
Make sure the CoP result and the exact warning shown are stored with every payment, ready for scoring and for claims.
Build one controlled interface for sharing fraud data, with a DPIA and clear lawful basis, so new sharing schemes plug in rather than being bolted on.
Keep the hold notices, the analyst decisions and the reasons in a form you could show the ICO.
Good answers are specific. Vague answers to any of these are worth following up.
Whether you need someone to lead the design or to support the architect you already have, let's talk it through.