PCI-DSSPayment Card Industry Data Security
Pipelines touching card data require tokenization, segmentation, annual SAQ-D audit and signed logs. Fraud models run in PCI-isolated environments.
Applied AI for payments, fraud & risk management.
Real-time fraud detection, behavioral analysis and risk management with proprietary models — not third-party dependent.
Overview
What we deliver in this vertical.
Payments · E-commerce · B2B
Fraud & Risk Management
Real-time fraud detection, behavioral analysis and risk management with proprietary models — not third-party dependent.
−68%chargeback
−54%false positives
<80msavg. latency
Applications
- 01Real-time scoring (<100ms)
- 02Graph and network analysis
- 03Multivariate anomaly detection
- 04AI-assisted investigation
- 05Regulator-ready audit
Positioning
Real fraud prevention lives in seconds, not in dashboards.
Instant payments, BNPL and digital-first checkout gave fraud a low-friction, high-velocity playing field. A proprietary, owned-data, case-auditable model is the only way to keep loss controlled without burning conversion. Off-the-shelf SaaS arrives late — and costs more when the attacker pivots.
By the numbers
What changes in fraud scoring with proprietary AI.
−0%marketplace chargeback with hybrid model
−0%false positives with semantic features
<0msmedian real-time scoring latency
+0%fraud capture without added friction
Under the hood
A stack that scores in <100ms.
Real-time fraud is a latency problem before it's a model problem. The stack below prioritizes light inference, pre-computed features and signed audit logs on every decision.
Models
- XGBoost / LightGBMDefault for real-time scoring (<50ms).
- Graph Neural NetworksOn relationship and device fingerprint networks.
- Auxiliary LLMCase review and human-readable explanation.
Features
- In-RAM feature store (Redis)For sub-ms reads.
- Velocity featuresRolling 1m / 5m / 1h / 24h windows.
- Device + behavioralFingerprint, session, typing pattern.
Pipeline
- Kafka + FlinkStream processing at scale.
- gRPC + ProtobufLow-latency internal communication.
- HSM + rotated keysPer-decision signature.
Compliance
Fraud prevention with AI — what the regulator looks at.
Transactional fraud crosses at least 4 regulatory frameworks. We treat each block decision as an auditable act: payload, model, version, features and justification all signed.
BACEN 4.658Cybersecurity Policy
Defines requirements for detection models in critical financial pipelines: BCP, incident management and risk classification before deployment.
LGPDAutomated decision and right to review
AI block is automated decision — requires right to human review, justification accessible to the data subject, and documented DPIA. We implement contestation endpoint by design.
Open FinanceBanking data sharing
Models consuming open finance data must respect granular consent, declared scope and immediate revocation. Consent audit runs in parallel with scoring.
In production
Results in fraud prevention.
Chargeback · marketplace
Model combining behavioral, relationship graphs and device fingerprint. Chargeback fell 68% with no proportional false-positive increase.
False positives · digital bank
Refinement of existing model with semantic features. False positives fell 54%, improving experience without losing fraud coverage.
Scoring latency · acquirer
Real-time scoring pipeline with 80ms median latency. Critical for payment flow without impacting checkout.
04 · LA AI Method
From hypothesis to operation.
Five steps that prevent eternal pilots. Each stage has deliverables, metrics and decision gates.
M1
Diagnostic
Opportunity map, estimated value, data readiness.
2–3 wksM2
Strategy
Prioritized roadmap, reference architecture, governance.
2 wksM3
Proof of Value
POC with production criteria and business metric defined.
4–6 wksM4
Deployment
Model, integrations, security, observability and UX.
6–12 wksM5
Continuous Operations
Evolution, fine-tuning, eval suite, cost per use, new features.
RecurringFrequently asked
About AI fraud prevention.
Real-time PIX?
Yes. PIX requires scoring in <300ms in most cases; our deployments sit around 80-150ms including enrichment.
Do you compete with Sift, Sardine, ClearSale?
Not directly. We work with customers who want their own model (not third-party dependency), whether for regulation, cost or data ownership.
How do we audit block decisions?
Each decision has feature contributions, versioned model and signed payload. In case of contest, the exact decision can be reproduced.
What about new fraud patterns?
Models have periodic retraining + drift monitoring. New patterns trigger drift alert before materially affecting performance.