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BritonOne Technology
Solutions · Financial Crime

Catch fraud the moment it moves, without declining good customers.

Real-time monitoring, explainable AI scoring, and audit-ready evidence, built for FCA- and PRA-regulated banks, insurers, and payment firms.

Solutions we provide

One engine watching every transaction, ready to act.

Real-time scoring, explainable decisions, and the model-risk evidence regulators ask for, in one service.

  • Real-time transaction scoring

    Every payment scored inline at authorisation, in under 150ms: fraud stopped before it settles, not flagged next morning.

  • Anomaly & pattern detection

    Models learn each customer's normal behaviour and surface what rigid rules miss: takeover, card-not-present rings, mule networks.

  • Explainable decisioning

    Every score carries its reason: feature attribution and clear decline codes keep Consumer Duty reviews and customers informed.

  • Audit-ready model evidence

    Model cards, traceability matrices, and a per-decision audit log are produced as you go, proof ready for your second line on request.

Fraud caught in milliseconds, good customers waved through, and every decision defensible on a reference call.

The problem with older fraud systems

Why the rule engine you run keeps missing, or over-blocking.

Most firms already run fraud controls. The older ones are brittle rules, overnight batches, and signals that never compound.

Static rules firing false positives

A rule per pattern until the estate is unmaintainable. Genuine customers get declined, and foreseeable-harm findings pile up.

Batch detection lands too late

Overnight scoring means the payment has already cleared. By the morning run, the money (and the trust) has gone.

Siloed signals miss the pattern

Fraud, AML, and onboarding each watch their own corner, so the mule network visible across all three never surfaces.

Why it matters

Benefits of real-time fraud detection

What changes for your firm once scoring, monitoring, and evidence run as one service.

  • Real-time scoring
  • Fewer false positives
  • Hidden patterns caught
  • Transactions protected
  • Models that adapt
  • Audit-ready evidence
Why BritonOne Technology

Why regulated firms trust us to score their transactions.

Fraud detection for supervised banks and insurers is the work we do most.

Rows of transaction nodes flowing across a dark platform into a hexagonal AI scoring engine (legitimate nodes in cyan, suspicious nodes flagged bright orange) with a 0.97 risk-score readout above the core
1

An engine built around your fraud, not a generic score

We engineer models on your own transactions and behaviour, with the explainability and model-risk evidence your second line reviews from day one.

2

Inside your own cloud

We work in your AWS, Azure, or GCP: your keys, your access. No transaction data leaves your estate.

3

Evidence built in as we go

Model cards, decision logs, and the supervisor pack are produced at every step, never scrambled before a review.

4

One senior team, around the clock

Scoring, investigation, and retraining come from one experienced team that stays with you, cover day and night as fraud lands.

A dark fraud-operations console with a live analytics dashboard, linked by glowing orange pathways to four control nodes (investigate, score, configure, and retrain) staffed around the clock
How we build it

Our way of building your fraud detection.

A clear, staged path from first look to real-time cover. Every stage leaves model-risk proof behind.

  1. 1

    Map the flows

    We learn your transaction journeys, fraud surfaces, and the false-positive limits you must stay inside.

  2. 2

    Engineer the models

    We build a feature store and train explainable scoring models tuned to your own customers and fraud patterns.

  3. 3

    Wire the stream

    Transaction feeds, scoring engine, and case management join into one inline decision plane, with a rule fallback.

  4. 4

    Tune the thresholds

    We run shadow-mode against live traffic and dial the catch-versus-false-positive balance to your risk appetite.

  5. 5

    Go live in real time

    Real-time scoring begins, with a model-risk evidence pack and a quarterly retrain cadence from day one.

Success stories

Programmes we have shipped

Loss and fraud analytics for a fintech
live
Fintech

Loss and fraud analytics for a fintech

Live loss-and-fraud analytics replaced a two-day report at a fintech, surfacing emerging fraud same-day.

dbtBigQueryLookerFivetran
Country · UK
Red-team engagement against a fintech app
3 critical
Fintech

Red-team engagement against a fintech app

Found and helped close three account-takeover paths before a fintech's launch.

Burp SuiteFridaMobSFOWASP MASVS
Country · UK
On-chain intelligence platform for a financial-intelligence unit
Self-service Web3
Government & Public

On-chain intelligence platform for a financial-intelligence unit

Turned raw on-chain data into a self-service intelligence dashboard that surfaces suspicious wallet activity in seconds for a public financial-intelligence unit.

The GraphEthereumGraphQLNode.js
Country · UK
Self-healing incident remediation for a manufacturer's OT platform
7 min
Manufacturing

Self-healing incident remediation for a manufacturer's OT platform

Cut median incident remediation from 41 to 7 minutes across a manufacturer's OT platform, with a human approval gate on every action.

GoKubernetesLangGraphPrometheusOT connectors
Country · DE
AI governance and model-risk framework for a pharma company
9 weeks
Pharma

AI governance and model-risk framework for a pharma company

Stood up a board-ready AI governance and model-risk framework for a pharma company in 9 weeks, cleared at first internal audit.

GovernanceModel riskGxPEU AI Act
Country · CH
Real-time operations dashboard for a logistics operator
120ms
Logistics

Real-time operations dashboard for a logistics operator

Sub-120ms streaming operations dashboard across 40 control desks for a logistics operator.

ReactWebSocketsRustRedis
Country · UK
Field-service app for a manufacturer's engineers
31%
Manufacturing

Field-service app for a manufacturer's engineers

Offline-first engineer app cut job-completion admin 31% for a manufacturer's field team.

FlutterDartSQLiteGCP
Country · UK
Datacenter exit for an NHS hospital group
900
Healthcare

Datacenter exit for an NHS hospital group

Migrated 900 clinical workloads off two datacentres to the cloud with zero downtime on patient-facing systems.

AzureTerraformAzure MigrateAnsible
Country · UK
Secure landing zone for a government department
7 weeks
Government & Public

Secure landing zone for a government department

Stood up a compliant, multi-account landing zone for a government department in 7 weeks.

AWSControl TowerTerraformSCPs
Country · UK
GxP-compliant CI/CD for a pharma manufacturer
8x
Pharma

GxP-compliant CI/CD for a pharma manufacturer

Delivered validated, GxP-compliant CI/CD, cutting release lead time 8x with full audit evidence.

GitLab CITerraformKubernetesAnsible
Country · CH
24/7 SRE for a telemedicine platform
99.98%
Telemedicine

24/7 SRE for a telemedicine platform

Ran a telemedicine platform to a 99.98% SLO with a follow-the-sun on-call.

KubernetesPrometheusPagerDutyTerraform
Country · UK
Data governance and catalogue for a pharma manufacturer
100%
Pharma

Data governance and catalogue for a pharma manufacturer

Catalogued and lineage-traced 100% of GxP-regulated data domains for a pharma manufacturer.

CollibradbtSnowflakeOpenLineage
Country · CH
Penetration test for a hospital estate
61
Healthcare

Penetration test for a hospital estate

Surfaced 61 exploitable paths across a hospital estate, prioritised by patient-safety impact.

ProwlerScoutSuiteCobalt StrikeNessus
Country · UK
Zero-trust rollout for a government agency
25k
Government & Public

Zero-trust rollout for a government agency

Rolled zero-trust access to 25,000 civil servants without a productivity dip.

OktaZscalerEntra IDTerraform
Country · UK
Privileged access overhaul for a manufacturer
90%
Manufacturing

Privileged access overhaul for a manufacturer

Cut standing privileged access 90% across a manufacturer's IT and OT estate.

CyberArkEntra PIMTerraform
Country · DE
GxP and Annex 11 compliance for a pharma manufacturer
12 weeks
Pharma

GxP and Annex 11 compliance for a pharma manufacturer

Reached validated Annex 11 compliance for a pharma manufacturer in 12 weeks.

GovernanceGAMP 5Annex 11Vanta
Country · CH
DORA readiness for an insurer
11 weeks
Insurance

DORA readiness for an insurer

Reached DORA operational-resilience readiness in 11 weeks for an insurer.

GovernanceDORAResilience testing
Country · DE
Threat modelling a connected vehicle
R155
Automotive

Threat modelling a connected vehicle

Mapped and mitigated attack paths for a UNECE R155-regulated connected vehicle.

STRIDEISO 21434UNECE R155Threat modelling
Country · DE
API testing for an IoT device platform
0 contract breaks
Manufacturing

API testing for an IoT device platform

Validated device, telemetry, and notification APIs under live conditions, shipping a release with zero contract breaks across connected consumers for an IoT platform.

PostmanRESTWebSocketNewman
Country · DE
Functional QA on the release train for a task-management SaaS
100% critical coverage
Logistics

Functional QA on the release train for a task-management SaaS

Hands-on regression and exploratory testing of boards, dashboards, and role-based workflows kept every critical path stable through fortnightly releases, holding critical-path coverage at 100% for a UK logistics-operations SaaS.

WebTestRailXrayJira
Country · UK
Load and soak testing for an enterprise cloud platform
99.9%
Government & Public

Load and soak testing for an enterprise cloud platform

Validated a 99.9% uptime SLA under peak load, so the cloud portal and provisioning APIs held through demand spikes without incident.

k6JMeterGrafanaPrometheus
Country · US
Multi-cloud security and misconfiguration assessment
Audit-ready posture
Fintech

Multi-cloud security and misconfiguration assessment

Assessed the attack surface and misconfigurations across AWS, Azure, and GCP and mapped every finding to compliance, leaving the estate audit-ready.

AWSAzureGCPScoutSuite
Country · DE
On-chain compliance and audit-readiness platform for a protocol
Audit-ready in weeks
Fintech

On-chain compliance and audit-readiness platform for a protocol

Automated evidence collection and continuous monitoring got a protocol audit-ready in weeks rather than months, with a complete controls trail behind every review.

SlitherFoundryOpenZeppelinEthereum
Country · CH
Tokenised carbon credits for an ESG reporting platform
94% fewer data errors
Government & Public

Tokenised carbon credits for an ESG reporting platform

On-chain carbon-credit issuance and retirement contracts gave auditors tamper-proof evidence and cut reporting data errors by 94% for an ESG reporting platform.

SolidityERC-1155HardhatPolygon
Country · DE

Who we are

About us

BritonOne Technology is a full-cycle engineering company that builds and operates production software for regulated estates. Since 2017, we have shipped programmes that clear audit on the first pass across banking, insurance, wealth, healthcare, and biotech. Our teams pair deep domain knowledge with disciplined engineering, treating compliance, security, and resilience as first-class deliverables. From architecture through to live operations, we stay accountable for the systems we build, measuring success by uptime, audit outcomes, and defensible business results.

60+Senior engineers across UK and EU

Why choose us

Engineer experience, average9+ yrs
Specialist replacement window48h
Code and IP ownership, day one100%
Surprise invoicesZero
Reference calls, not slideware

What fraud teams say after go-live.

Every quote comes from a programme we shipped, and a reference you're welcome to call.

Patterns our rules missed, caught in weeks.

We had tuned a rule engine for nine years. BritonOne Technology's shadow-mode caught patterns our rules had missed inside the first three weeks: cross-customer rings we never knew were there.

Head of FraudTier-2 UK retail bank
Common questions about fraud detection

What heads of fraud and CROs ask before commissioning a rebuild.

Frequently asked questions

Three differences. (1) We are model-led rather than rule-led: the engine learns each customer's behaviour and detects anomalies and cross-customer patterns rigid rules never see, so the false-positive rate falls while catch rate rises. (2) Scoring is inline and real-time (sub-150ms p99 at authorisation), not an overnight batch that flags fraud after the money has cleared. (3) The model-risk evidence pack is engineered as a primary deliverable, not produced on request. Rules remain (as a transparent fallback path and a floor under the model), but the model carries the decision.