Financial AI that regulators won't flag.
From fraud detection to credit scoring to regulatory reporting - we build financial AI systems with the explainability, auditability, and compliance posture your regulators expect.
What we build
Fraud Detection
Real-time transaction scoring, anomaly detection, and adaptive fraud pattern recognition. Sub-100ms inference latency for card-present and digital transactions.
Credit Risk Scoring
Alternative data credit models, LTV prediction, and scorecard development - with SR 11-7 compliant model documentation and validation evidence.
Document Intelligence
LLM-powered extraction from financial contracts, statements, invoices, and regulatory filings. Structured output with confidence scoring for downstream automation.
Regulatory Reporting
Automated data pipelines for CCAR, stress testing, FINREP, and AML reporting - reducing manual compilation time by 80–95% across reporting cycles.
Trading & Risk Analytics
Market microstructure analysis, portfolio risk dashboards, and signal generation pipelines for quant teams - with real-time data ingestion from major exchanges.
Compliance architecture
Explainability
- Model cards for every production model
- SHAP-based feature attribution
- Adverse action notices (FCRA-compliant)
- Audit trails from input to decision
- Plain-language model documentation
Data Security
- AES-256 encryption at rest
- TLS 1.3 in transit
- Tokenisation for PII and financial data
- Key management (HSM / AWS KMS)
- SOC 2 Type II audit-ready architecture
Regulatory Alignment
- SR 11-7 model risk management framework
- EU AI Act high-risk system controls
- Fair lending analysis (ECOA / Reg B)
- GDPR data subject rights implementation
- MiFID II record-keeping requirements
Real-time vs batch
The right architecture depends on your latency requirements. We help financial teams choose correctly - before they build wrong.
Real-time
< 100ms P99Use cases
Fraud detection, transaction monitoring, AML screening
Millisecond decisioning required before transaction settlement. Latency directly impacts customer experience and fraud loss rates.
Batch / Near-real-time
Minutes to hoursUse cases
Credit scoring, risk reporting, model retraining
Accuracy requirements outweigh latency constraints. Larger feature sets and more complex models are viable when seconds or hours are acceptable.
Model risk management
SR 11-7 sets the standard for model risk management in US financial services. We build to it - not around it.
Model Inventory
All production models documented with intended use, risk tier, performance benchmarks, and review schedule.
Independent Validation
Model validation performed by team separate from development. Replication of key results, challenger model comparison, and sensitivity analysis.
Ongoing Monitoring
Production model performance tracked against PSI, CSI thresholds. Automated alerts on drift. Scheduled revalidation on defined triggers.
Governance Committee
Model approval, exception management, and retirement decisions escalated to governance committee with documented sign-off.
"LLM contract review pipeline saving £2.1M/year for a UK insurance broker."
RAG-powered extraction from policy documents, endorsements, and cover notes. Replaced a 12-person manual review team. Full audit trail on every extraction decision - GDPR compliant, MiFID II record-keeping aligned.
Fintech AI stack
Numbers that matter in financial AI deployments.
< 80ms
P99 fraud decision latency for card-present transactions
99.97%
uptime SLA on real-time AML screening pipelines
83%
reduction in false-positive fraud alerts after model tuning
£2.1M
annual cost saving from LLM document review automation
Without AI vs. with StartxLabs.
The fintech AI stack, layer by layer.
“We replaced a 12-person manual review team with an LLM pipeline that processes 400 policy documents a night - and every extraction decision has a full audit trail.”
400+
Documents processed nightly
£2.1M
Annual cost saving
99.3%
Field extraction accuracy
Compliant
MiFID II record-keeping