πŸ’° FinTechOn TimeπŸ“‹ Fixed Price

FinTech UK RegTech AML Transaction Monitoring Platform

UK6 min readUpdated June 2026
Region
UK
Contract
Fixed Price
Tech Stack
11 Technologies
IP
100% transferred

Project Overview

A UK FCA-authorised payment institution (FCA EMI, GBP8.4B payment volumes, 280,000 business customers) needed ...

Technology Stack

ReactNode.js/FastifyPostgreSQLPython (XGBoostIsolation ForestNetworkX graph ML)ComplyAdvantage APINCA goAML APIAWS Batch (daily sanctions rescreening)AWS SageMaker (ML inference)AWS eu-west-2

Compliance & Standards

AML MLRs 2017FCA JMLSG GuidanceTerrorist Financing Act UKHMRC DAMLNCA ELMER typologiesUN/OFAC/HMRC SanctionsUK GDPRISO 27001Cyber Essentials Plus
Step 01

The Challenge

A UK FCA-authorised payment institution (FCA EMI, GBP8.4B payment volumes, 280,000 business customers) needed to build a new AML transaction monitoring platform -- replacing a rules-only legacy system that was generating too many false positives (8,400 alerts per week, 3-day analyst review backlog). AML MLRs 2017, FCA JMLSG Guidance, Terrorist Financing (Prevention) Act UK, HMRC DAML (Defence Against Money Laundering), NCA ELMER typologies, Sanctions (OFAC, HMRC, UN), UK GDPR, Cyber Essentials Plus, ISO 27001. Budget GBP150,000.

Step 02

Our Approach

Powered Transaction Monitoring

ML transaction monitoring replacing rules-only: (1) feature engineering (transaction amount, frequency, counterparty diversity, geographic spread, time-of-day pattern, round-number ratio, cash-equivalent transactions), (2) unsupervised anomaly detection (Isolation Forest -- detects unusual transaction patterns per customer baseline), (3) supervised classification (XGBoost -- trained on labelled historic alerts: confirmed SAR vs false positive), (4) network analysis (graph ML -- detect circular transactions, beneficial ownership chains, counterparty networks), (5) alert prioritisation (ML risk score replaces rules threshold -- top 10% highest-score alerts reviewed first).

False positive rate target

reduce from 94% (rules-only) to below 40% (ML + rules combined).

NCA ELMER typologies

ML features aligned to NCA published money laundering typologies.

Sanctions screening at scale

  • 1ComplyAdvantage API (customer onboarding screening and daily rescreening -- OFAC, HMRC Sanctions, UN, EU retained),
  • 2HMRC Deferred Debit Prevention (DAML -- pre-transaction sanctions check for high-risk payments),
  • 3fuzzy name matching (Jaro-Winkler distance -- catches transliteration variants),
  • 4PEP (Politically Exposed Person) screening (ComplyAdvantage PEP data -- enhanced due diligence trigger),
  • 5adverse media screening (ComplyAdvantage adverse media API -- negative news about customer or counterparty).

Daily rescreening

280,000 customers rescreened daily against updated sanctions lists (batch processing via AWS Batch overnight -- 6-hour processing window).

SAR Workflow and NCA goAML

SAR (Suspicious Activity Report) workflow: FCA expects SARs filed within 24 hours of decision to file.

SAR workflow

  • 1alert generated (ML score above SAR threshold),
  • 2analyst review (analyst reviews alert evidence -- transaction history, network graph, news),
  • 3SAR decision (file SAR or close alert -- documented rationale either way),
  • 4SAR draft (goAML XML format -- customer details, suspicious transactions, narrative),
  • 5NCA goAML submission (API submission -- goAML acknowledgement reference).

TIPPING OFF PREVENTION

system must not allow customer to know SAR has been filed (Proceeds of Crime Act 2002 s.333A).

Tipping off control

SAR workflow on separate access-controlled subsystem -- customer-facing staff cannot see SAR status.

FCA JMLSG Guidance Compliance

FCA JMLSG (Joint Money Laundering Steering Group)

Guidance

  • industry-wide best practice guidance for UK financial services AML.
  • JMLSG-aligned controls: (1) risk-based approach (ML risk score enables genuine risk stratification -- not all customers treated equally), (2) customer risk assessment (ML features used to calculate customer AML risk score -- updates with each transaction), (3) enhanced due diligence triggers (ML identifies customers with elevated risk -- triggers EDD review), (4) training and awareness (FCA expects all staff who handle payments to complete AML training -- LMS tracking integrated), (5) periodic review (ML model retrained quarterly -- model drift monitored, JMLSG expects continuous improvement).

HMRC DAML

Defence Against Money Laundering consent -- for transactions involving proceeds of crime where firm has knowledge or suspicion.

Step 03

The Results

Platform live at 26 weeks, GBP138,000. 280,000 customers.

GBP8.4B payment volumes.

Alert volume: 8,400/week to 2,100/week (75% reduction).

False positive rate: 94% to 38%.

Analyst review backlog: 3 days to same-day.

Confirmed SAR rate: 2.8% of alerts (was 0.6% rules-only).

SARs filed: 148 in year one, all within 24 hours.

Sanctions screening: 280,000 customers rescreened daily.

JMLSG compliance confirmed.

ISO 27001 maintained.

Alerts 8,400 to 2,100 per week.

False positives 94% to 38%.

Analyst backlog 3 days to same-day.

Confirmed SAR rate 2.8% vs 0.6% rules-only. 148 SARs all within 24 hours.

The false positive reduction -- from 94% to 38% -- was not just an efficiency gain.

It was a quality improvement.

Analysts reviewing 94% false positive alerts become desensitised.

They stop seeing suspicious patterns because every alert looks like noise.

Reducing false positives to 38% restored analyst alertness.

The 148 SARs in year one -- compared to 22 the previous year -- came from analysts who could see the signal again. -- Chief Compliance Officer, UK Payment Institution

Project Details

Sector
FinTech
Country
UK
Status
On Time
Contract
Fixed Price
Tech Stack
11 Technologies
Reading Time
6 min
IP Ownership
100% transferred
Last Updated
June 2026
Written By
ClickMasters Case Study Team
Reviewed By
James Whitmore, CTO

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