Financial AI that survives
the regulator and the auditor

KYC Automation

Customer identification, document verification and automated onboarding.

• OCR for identity documents
• Liveness detection
• PEP and sanctions list screening
• Transparent risk scoring

AML and Transaction Monitoring

Real-time detection of suspicious patterns.

• Behavioral analytics
• Sanctions screening (OFAC, EU, UN)
• SAR / STR generation
• False-positive reduction

Credit Decisioning (Explainable)

Credit decisions explainable to every customer and to the regulator.

• SHAP / LIME explanations
• Adverse action reason codes (ECOA / FCRA)
• Bias monitoring and fair-lending checks
• Documented Model Cards (Mitchell et al. 2019)

Real-time Fraud Detection

Block suspicious transactions within sub-100ms P95.

• Device fingerprinting
• Velocity checks
• Graph analysis
• Self-learning models with chargeback feedback

Banking AI Assistant

Bank chatbot with RAG over your regulatory and product documents.

• RAG over OCC, FFIEC, EBA guidance
• Secure customer history retrieval
• Multi-language (EN / HE / RU)
• Human handoff on escalation paths

Model Governance

Documentation, monitoring and control of every production model.

• Model registry (MLflow / Vertex / SageMaker)
• Drift detection (PSI, KS, concept drift)
• Champion / Challenger flow
• Regulatory reporting templates

US: SR 11-7, OCC, FFIEC, CFPB

Federal Reserve SR 11-7 Model Risk Management Framework, OCC 2011-12, FFIEC IT exam handbook, CFPB UDAAP, ECOA / Reg B for credit decisions, FCRA for adverse action.

EU: PSD2, GDPR, EU AI Act

Strong Customer Authentication under PSD2 RTS, Open Banking APIs, EU AI Act readiness (Article 6 risk classification / 9 risk management / 10 data governance / 13 transparency / 14 human oversight / 15 accuracy), GDPR DPIA for high-risk processing.

Operational controls and segregation of duties

Operational and security controls, full audit trail, segregation of duties, privileged access management - built to support a customer-side external audit. PCI-DSS where card data is in scope.

KYC + AML automation

What the industry reports: Banks running comprehensive onboarding automation report 60-80% reduction in compliance-team workload and time-to-decision (McKinsey 2024 banking ops report).

Where we come in: OCR on identity documents, liveness detection, PEP and sanctions screening, and explainable risk scoring (SHAP) - all leaving an audit trail the regulator can read.

• End-to-end automated onboarding workflow
• Every decision explainable to three audiences: customer, regulator, internal audit
• Champion / Challenger flow via MLflow - not a "frozen model"
• Built for the standards: SR 11-7, OCC 2011-12, PSD2, GDPR

Real-time fraud detection

What the industry reports: Moving from rule-based models to hybrid (behavioral + GBM) reduces false-positives by 30-50% while preserving recall (Bain payments tech 2024).

Where we come in: Designing the feature pipeline that runs at transaction time (sub-100ms P95), graph-based velocity checks, and a feedback loop from chargeback into the next model.

• Decision latency sub-100ms - fits in-line with card authorization
• Self-learning loop from chargeback - not just "first model"
• Model registry and drift monitoring built in
• Bias monitoring by demographic - part of fair lending

Explainable credit decisioning

What the industry reports: Credit teams using SHAP / LIME explanations reconstruct the model's decision in 95%+ of cases, required by ECOA + EU AI Act Article 13 (FCA + EBA papers).

Where we come in: Retro-fitting SHAP on existing XGBoost, generating Adverse Action reason codes tailored to the US regulator (Reg B), and a full Model Card per Mitchell et al. 2019.

• Explainable end-to-end - not "black box with manual override"
• Adverse Action reasons (ECOA Reg B) for the credit applicant
• Model Card per Mitchell et al. 2019 - presentable to a model committee
• Built to update against EU AI Act Articles 9-15 once enforcement starts