Financial services · Playbook 02

I've worked in this sector

Payment fraud and APP scam detection

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.

  • Predictive ML
  • Agents with tools
  • Human in the loop
  • Event-driven integration
  • PSR APP reimbursement (moving to the FCA)
  • Payment Services Regulations 2017
  • FCA Consumer Duty
  • ECCTA 2023
  • UK GDPR

At a glance

The problem
Scams are authorised by the customer, so they look like normal payments, and the sending firm now pays half of every reimbursed loss
Typical volume
Around 20 million outbound Faster Payments a month, 1,500 alerts reviewed a day, and 800 APP scam claims a month
Who's involved
Fraud operations, the fraud strategy team, customer contact, claims, payments engineering, model risk, compliance, data protection
Pilot
8–10 weeks in shadow mode, after a 3–4 week discovery
Platforms
AWS, Azure, or a sovereign or on-prem stack

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.

The business problem

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.

What AI changes

  • Every outbound payment is scored in milliseconds by a fast, explainable model, alongside the rules you already run. Generative AI is never in the payment path.
  • Only payments in the highest-risk band are held automatically, under the payment delay power and rules a person approved. The customer is told why and what happens next, the same day.
  • Every other alert goes to an analyst before any hold. The payment isn't held while they look: the customer sees a scam warning in the app, tailored to the pattern that scored, and must confirm before it goes. The analyst decides whether anything more is needed: contacting the customer, holding a later payment, or trying a recall.
  • Each alert, and each automatic hold, reaches an analyst with a short summary: the main reasons it scored high, the scam pattern it most resembles, and the evidence behind each point.
  • The analyst gets a suggested set of questions for the customer conversation, drawn from approved scripts for that scam type, so calls are consistent and nobody is accused.
  • Incoming payments are watched too. Accounts that look like money mules are flagged to a person before the money moves on.
  • Reimbursement claims arrive with the evidence already gathered and the deadlines tracked. A person makes every decision on a payment, an account or a claim, and every step is logged.

How success is measured

Agree these, and measure the baseline, before anything is built. Example targets are typical, not promises.

APP scam losses

Value lost per million outbound payments, split by scam type and by where the scam started.

ExampleDown by a third against last year

Alert precision

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

Good payments delayed

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

Analyst time per alert

Time from opening an alert to a decision, including the customer call.

ExampleDown by half

Claims decided on time

Reimbursement decisions made within five business days, or within 35 when the clock is stopped for good reason.

ExampleEvery claim, every month

Override rate

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

UK regulation and controls

Cross-sector rules are on the UK regulation map.

  • PSR (moving to the FCA)APP scams reimbursement requirement, live since 7 October 2024

    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.

  • HM Treasury and the FCAPayment Services Regulations 2017, as amended in 2024 (the payment delay power), and FCA guidance FG24/6

    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.

  • PSRConfirmation of Payee (Specific Direction 17)

    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.

  • FCAConsumer Duty (PRIN 2A)

    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.

  • ParliamentEconomic Crime and Corporate Transparency Act 2023

    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.

  • ICOUK GDPR and automated decisions (as amended by the Data (Use and Access) Act 2025)

    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.

The solution

Showing AWSAzuresovereign / on-prem services. Switch with the toggle; every diagram and table on this page follows it.

Payment fraud and APP scam detection: reference architecture
  1. 01Channels

    • Mobile app and online banking
    • Inbound Faster Payments
    • Customer calls and chat
  2. 02Intake

    • API GatewayAPI ManagementKong, or your existing API gatewayScoring API
    • Amazon MSKAzure Event HubsApache KafkaPayment event stream
    • Amazon S3Blob StorageCeph, or IONOS S3 Object StorageEvidence store
  3. 03Intelligence

    • SageMaker AI endpointAzure Machine Learning online endpointMLflow and Kubeflow on KubernetesReal-time scoring model
    • Amazon NeptuneAzure Cosmos DB for Apache GremlinJanusGraph on KubernetesMule network analysis
    • Agent
      Bedrock AgentCoreFoundry Agent ServiceLangGraph on Kubernetes, with open-weight models via vLLMAlert investigation agent
    • Agent
      Bedrock AgentCoreFoundry Agent ServiceLangGraph on Kubernetes, with open-weight models via vLLMClaims assistant
  4. 04Orchestration

    • Step FunctionsLogic Apps / Durable FunctionsTemporalHold and review workflow
    • EventBridge + SQSEvent Grid + Service BusApache Kafka or RabbitMQEvents and queues
  5. 05Systems of record

    • Payments hub and core banking
    • Existing fraud engine and case management
    • Reimbursement claims system
  6. 06People

    • Person
      Fraud analyst: hold, release or refuse
    • Person
      Claims handler: reimburse or decline
    • Person
      Vulnerability and financial crime teams
  • Cloud or platform service (AWSAzuresovereign / on-prem)
  • AI agent
  • A person decides
  • Your existing systems
  • Outside your organisation

Components, side by side

Payment fraud and APP scam detection: components on AWS, Azure and a sovereign or on-prem stack
LayerAWSAzureSovereign / 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 StreamsAPI Management, Azure Event HubsKong 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 endpointAzure Machine Learning managed online endpointMLflow 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 StoreAzure Machine Learning managed feature storeFeast 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 NeptuneAzure Cosmos DB for Apache GremlinJanusGraph, 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 BedrockMicrosoft FoundryOpen-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 BasesFoundry IQ, built on Azure AI SearchOpenSearch, or PostgreSQL with pgvector
Agent runtimeThe original Bedrock Agents closed to new customers on 30 July 2026; new builds use AgentCore.Amazon Bedrock AgentCoreFoundry Agent ServiceAn 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 FunctionsLogic Apps or Durable FunctionsTemporal, 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 platformAPI Management, plus your existing integration platformKong 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 toolIn the analysts' existing fraud case management toolIn the analysts' existing fraud case management tool
GuardrailsBlocks drafts that accuse the customer, promise an outcome, or mention a suspicious activity report.Bedrock GuardrailsFoundry guardrails (Azure AI Content Safety)Open-source guardrails (e.g. NeMo Guardrails, Llama Guard)
Identity, keys, data protectionIAM Identity Center, KMS, MacieEntra ID, Key Vault, PurviewKeycloak 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 pipelineAzure Monitor, Log Analytics, Azure Machine Learning model monitoringPrometheus, 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 WestYour 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 →

Hybrid and on-prem

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.

The agents

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

May do

  • Read the alerted or held payment, its score and its reason codes
  • Look up the payee's history, the CoP result, recent logins and device changes
  • Check the receiving account against mule signals from the network analysis
  • Search the scam typology library and suggest the closest pattern
  • Draft an alert summary with evidence, and suggested questions from the approved scripts

May not do

  • Hold, release or refuse a payment
  • Freeze or close an account
  • Contact the customer or the receiving firm
  • Share data with another firm
  • Raise or mention a suspicious activity report

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

May do

  • Gather the claim evidence: the payment, CoP result, warnings shown, call notes and the alert history
  • Request the receiving firm's information through the claims system
  • Track the five-day and 35-day deadlines and warn before they slip
  • Check for vulnerability flags and route those claims to a specialist
  • Draft the decision letter and the case note for the handler

May not do

  • Decide, part-pay or decline a claim
  • Apply the excess or make a finding of gross negligence
  • Stop the clock
  • Send anything to the customer

Where a person signs offA claims handler decides every claim. Declines and gross negligence findings get a second review before the customer is told.

Middleware and integration

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.

Process and people

  • A fraud strategy owner approves the hold rules and thresholds, including the narrow highest-risk band that is held automatically, and signs off every change. The model suggests; the approved rules decide what is held automatically, and analysts decide every other hold.
  • An agreed pick-up time for alerts and an agreed release target, so alerts are reviewed quickly and held payments aren't left waiting to the end of the fourth business day by default.
  • Approved call scripts per scam type, owned by fraud and compliance, with a weekly review of a sample of real calls.
  • A named owner for the model, the typology library and each prompt, with change control and model risk review like any other model.
  • A weekly look at scam types the model is missing, including where scams moved to: crypto, international payments and other routes outside the reimbursement rules.
  • Claims QA on a sample of decisions each week, checking consistency, deadlines and how vulnerability was handled.

Delivery plan

  1. 3–4 weeks

    Discovery

    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.

  2. Gate · must be true to passBaseline agreed, latency budget confirmed with payments engineering, DPIA started, model risk and compliance involved.

  3. 8–10 weeks

    Pilot

    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.

  4. 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.

  5. 4–6 weeks

    Production

    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.

  6. Gate · must be true to passMonitoring, support model, rollback switch and analyst training in place.

  7. Ongoing

    Scale

    Add the claims assistant, inbound mule detection, then card and account takeover fraud.

  8. Gate · must be true to passEach new use passes the same shadow-mode gate.

Roles and RACI

Who is responsible, accountable, consulted and informed, by activity
ActivityHead of fraudProduct ownerSolutions architectI can fill thisTechnical PMI can fill thisEngineersI can fill thisModel riskCompliance & DPOSecurityI can fill this
Business case and benefitsAccountableResponsibleConsultedConsultedInformedConsultedConsultedInformed
Target architecture and latency budgetConsultedConsultedAccountable and ResponsibleConsultedConsultedConsultedInformedConsulted
Hold rules and thresholdsAccountableResponsibleConsultedInformedConsultedConsultedConsultedInformed
Model validationConsultedConsultedConsultedInformedResponsibleAccountableConsultedInformed
DPIA, Consumer Duty and reimbursement reviewConsultedConsultedConsultedInformedInformedConsultedAccountable and ResponsibleConsulted
Build and integrationInformedConsultedAccountableResponsibleResponsibleConsultedInformedConsulted
Go-live decisionAccountableResponsibleConsultedResponsibleInformedConsultedConsultedConsulted
Run, monitor and improveAccountableResponsibleConsultedConsultedResponsibleConsultedConsultedConsulted

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

Target architecture and latency budget

Head of fraud
C · Consulted
Product owner
C · Consulted
Solutions architect
A/R · Accountable and Responsible
Technical PM
C · Consulted
Engineers
C · Consulted
Model risk
C · Consulted
Compliance & DPO
I · Informed
Security
C · Consulted

Hold rules and thresholds

Head of fraud
A · Accountable
Product owner
R · Responsible
Solutions architect
C · Consulted
Technical PM
I · Informed
Engineers
C · Consulted
Model risk
C · Consulted
Compliance & DPO
C · Consulted
Security
I · Informed

Model validation

Head of fraud
C · Consulted
Product owner
C · Consulted
Solutions architect
C · Consulted
Technical PM
I · Informed
Engineers
R · Responsible
Model risk
A · Accountable
Compliance & DPO
C · Consulted
Security
I · Informed

DPIA, Consumer Duty and reimbursement review

Head of fraud
C · Consulted
Product owner
C · Consulted
Solutions architect
C · Consulted
Technical PM
I · Informed
Engineers
I · Informed
Model risk
C · Consulted
Compliance & DPO
A/R · Accountable and Responsible
Security
C · Consulted

Build and integration

Head of fraud
I · Informed
Product owner
C · Consulted
Solutions architect
A · Accountable
Technical PM
R · Responsible
Engineers
R · Responsible
Model risk
C · Consulted
Compliance & DPO
I · Informed
Security
C · Consulted

Go-live decision

Head of fraud
A · Accountable
Product owner
R · Responsible
Solutions architect
C · Consulted
Technical PM
R · Responsible
Engineers
I · Informed
Model risk
C · Consulted
Compliance & DPO
C · Consulted
Security
C · Consulted

Run, monitor and improve

Head of fraud
A · Accountable
Product owner
R · Responsible
Solutions architect
C · Consulted
Technical PM
C · Consulted
Engineers
R · Responsible
Model risk
C · Consulted
Compliance & DPO
C · Consulted
Security
C · Consulted

R ResponsibleA AccountableC ConsultedI Informed· not involved

Where I'd fit

Option A

Lead the architecture

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

Support your architect

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.

Where this goes wrong

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

On the horizon

Changes already announced or under way that affect this design. More in the UK horizon scan.

  1. December 2026 to May 2027
    The PSR's consultation on changes to APP reimbursement, following the independent evaluation published on 1 July 2026. It covers claims that run past 35 business days, guidance on the consumer standard of caution, civil disputes and me-to-me payments, and annual data on the platforms fraudsters use. A decision and revised directions are planned for May 2027, with up to six months to implement.

    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.

  2. Late 2026 to 2028
    The Financial Services and Markets Bill abolishes the PSR and moves its powers, including APP reimbursement, to the FCA. The Lords stages finished on 15 September 2026 and the Bill is now in the Commons. Commentators expect firms to feel the change from 2028.

    No redesign is needed. Map each control to the requirement, not the regulator, and watch for FCA consultations on a combined payments rulebook.

  3. From 31 December 2026 (proposed)
    Confirmation of Payee is now permanent. The PSR also proposed directing firms that offer CoP voluntarily, as a new group, from 31 December 2026.

    Make sure the CoP result and the exact warning shown are stored with every payment, ready for scoring and for claims.

  4. 2026 to 2029
    The Home Office Fraud Strategy 2026 to 2029 (9 March 2026) set up a public-private Online Crime Centre and plans to tackle barriers to information sharing. Banks and payment firms are pressing for tech and telecoms firms to share reimbursement costs. The government hasn't adopted that.

    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.

  5. 2026 to 2027
    The ICO's code of practice on AI and automated decision-making, required by regulations in force since May 2026.

    Keep the hold notices, the analyst decisions and the reasons in a form you could show the ICO.

Questions to ask your partner or vendor

Good answers are specific. Vague answers to any of these are worth following up.

Copy them into your next supplier meeting

  1. What is the latency budget for scoring, and what happens to a payment when the model doesn't answer in time?
  2. Which scam types does the model catch well, and which does it miss, measured on our own payments?
  3. Which payments are held automatically, who approved that band, and how are good customers protected from long delays?
  4. Which actions can the agents take without a person, and where is that enforced?
  5. How do drafts avoid tipping off a customer or accusing them?
  6. What does it cost to run per million payments scored, at our volume?

Planning something like this?

Whether you need someone to lead the design or to support the architect you already have, let's talk it through.

Sam Naseem
Sam NaseemBirmingham, UK · UK & Europe
Available now