Compliance AI that keeps up with regulators.
We build regulatory-aware AI systems for AML/KYC, transaction monitoring, and compliance reporting - designed to adapt as regulations change, not break when they do.
Talk to a compliance AI engineerWhat we build
AML / KYC Systems
Customer due diligence automation, beneficial ownership resolution, sanctions screening, and PEP matching - with explainable risk scoring and SAR generation assistance.
Regulatory Reporting AI
Automated data extraction, validation, and report generation for CCAR, FINREP, COREP, and EMIR reporting - with rule-based validation and audit trails.
Transaction Monitoring
Adaptive rules engine combined with ML anomaly detection for AML transaction monitoring. Tuned to your institution's transaction patterns to reduce false positives.
Risk Assessment Models
Counterparty credit risk, operational risk scoring, and concentration risk models - documented for SR 11-7 compliance and internal audit review.
Regulatory coverage
EU
GDPR
Data subject rights, lawful basis, DPA requirements
MiFID II
Record-keeping, best execution, transaction reporting
EU AI Act
High-risk AI system classification, conformity assessment
AMLD6
AML/CFT obligations, beneficial ownership, reporting
US
BSA / FinCEN
SAR filing, CTR reporting, CIP programme requirements
CCPA / CPRA
California consumer rights, opt-out, data broker rules
SR 11-7
Model risk management framework for bank models
FCRA
Adverse action, permissible purpose, dispute handling
UK
FCA
Consumer Duty, SM&CR, algorithmic trading surveillance
PRA
Supervisory Statement 1/23, model risk management
UK GDPR
Post-Brexit data protection, international transfers
MLRO obligations
Suspicious activity reporting, tipping-off rules
The explainability mandate
Financial regulators do not accept black-box AI. Here is why - and how we build systems that meet the bar.
Regulatory guidance (SR 11-7, SS1/23, EBA guidelines) requires banks to understand and explain model outputs.
Adverse action notices require human-interpretable reasons - 'the model said so' is not acceptable.
AML systems must produce evidence for SAR submissions that can withstand legal scrutiny.
Black-box models cannot be independently validated by model risk management teams.
We build compliance AI using interpretable model architectures where possible, SHAP attribution where complex models are necessary, and full decision trace logs as a standard output of every compliance system.
Continuous monitoring
Regulation change tracking
We build regulatory change feeds into compliance AI systems - flagging when rule changes affect model logic or thresholds.
Threshold auto-tuning
AML and transaction monitoring thresholds recalibrated quarterly using updated typology data and institution-specific patterns.
Performance drift alerts
Model performance tracked against PSI and population shift metrics - automated alerts before drift becomes a compliance risk.
Annual revalidation cycle
Structured revalidation of all compliance models on annual basis, with documentation suitable for internal audit and regulatory examination.
Data handling
PII Classification
All personal data classified, tagged, and tracked through the compliance AI pipeline. Automated PII detection in unstructured data ingestion.
Data Residency
Jurisdiction-specific data residency controls for EU, UK, and US customer data - enforced at infrastructure level, not just policy.
Retention & Deletion
Automated retention schedules aligned to BSA (5 years), GDPR (purpose limitation), and MiFID II (7 years) requirements.
Access Logging
Immutable audit trail on all access to sensitive compliance data - exportable for regulatory examination on demand.
"Transaction monitoring false positive rate reduced substantially - AML investigation team capacity increased without headcount increase."
ML-augmented rules engine deployed at a UK retail bank. Adaptive tuning reduced alert volume while maintaining SAR filing rates. Full MLRO sign-off documentation included at go-live.
Risk & compliance AI - impact metrics
Lower
AML transaction monitoring false positive rate at a UK retail bank
Higher
Investigation capacity without headcount increase post-ML deployment
< 200ms
Sanctions screening decisioning at scale for real-time payment flows
SR 11-7
Full model risk management documentation on every compliance model
Capability tiers
From standalone models to full compliance AI platforms
Point solution
A single compliance AI model integrated into your existing workflow.
AML transaction scoring model
SAR narrative generation
KYC document extraction
Sanctions name matching
SR 11-7 model card included
Integrated suite
End-to-end compliance intelligence layer across AML, KYC, and reporting.
Full AML/KYC pipeline
Regulatory report automation
Explainability layer (SHAP)
Continuous monitoring + drift alerts
Adverse action documentation
Managed platform
Compliance AI platform with ongoing model management and regulatory change tracking.
Everything in Integrated suite
Quarterly threshold recalibration
Regulatory change feed integration
Annual model revalidation cycle
Regulatory examination support
Business case
Estimated impact for a mid-size financial institution
AML false positive rate
Before
High false-positive rate; alert queue reviewed manually
After
Substantially lower false-positive rate. Investigators focus on real risk.
Analyst hours freed for higher-value review
KYC document review
Before
Manual, time-intensive document review by compliance analysts
After
Automated extraction + brief human confirmation for edge cases
Faster document review turnaround
Regulatory report preparation
Before
COREP/FINREP prep is a multi-week manual effort each quarter
After
Automated extraction and validation; team reviews exceptions only
Lower cost of regulatory report preparation
Model risk documentation
Before
SR 11-7 model cards assembled retroactively - MRM team bottleneck
After
Documentation generated alongside model development - audit-ready at go-live
Faster MRM approval
What we optimise for
Fewer false positives, better-evidenced investigations. Alert models are tuned so investigators spend their time on activity that actually warrants a look - not clearing a queue of noise.