The Discover AI Financial Crime Platform
A unified, modular intelligence suite engineered to connect data engineering, machine learning, scenario optimization, investigation, and second-line quality control into an auditable closed loop.
Interconnected Modules with Strict Segregation of Duties
Each module maintains strict authority boundaries to preserve investigator autonomy and regulatory compliance.
Data & Narrative Foundations
Governed ingestion, feature engineering, and narrative intelligence
Precision Risk Signals & Screening
Explainable machine learning models and entity graph intelligence
Scientific Scenario Tuning
Threshold sensitivity sweeps and operational capacity backtesting
Evidence-Led Investigations
Unified customer 360, transaction timelines, and assisted drafting
Quality Management & Calibration
Statistical sampling, objective defect taxonomies, and audit rigor
Verified Interoperability Workflows
Explore how data, evidence, and audit logs flow through the Discover AI platform during core financial crime operations.
AML Transaction Monitoring End-to-End Flow
From core banking ingestion to governed threshold improvement
Harmonize payment logs with temporal schemas and data contracts
Generate prioritized alerts with explainable tree-SHAP risk factors
Calibrate velocity and amount thresholds using out-of-time backtesting
Triage alerts, explore transaction timelines, and assemble evidence
Sample closed cases to verify evidence-to-conclusion consistency
Feed QA defect patterns into rule recalibration and model training
Sanctions, PEP & Entity Intelligence Flow
From watchlist delta screening to beneficial ownership disambiguation
Screen against versioned OFAC/EU/UN lists with cryptographic timestamps
Disambiguate matching entities using phonetic and multi-script algorithms
Traverse 1st, 2nd, and 3rd degree corporate ties to uncover hidden UBOs
Investigator verifies facts and documents match disposition rationale
Review sanctions clearance workpapers for regulatory audit compliance
Regulated Model Lifecycle (SR 11-7)
From point-in-time training to real-time drift telemetry
Extract training features with verified zero lookahead leakage
Train interpretable models with domain-enforced monotonic constraints
Stress-test boundaries and generate standardized Model Cards
Deploy scoring endpoints with full factor-attribution payloads
Automated monitoring of Population Stability Index (PSI/CSI)
System-of-Record Boundaries & Authority Matrices
Integration across Discover AI modules does not grant identical authority or access. Discover AI enforces strict role separation and system boundaries:
Primary Investigation Boundary (CMS)
Investigative notes, findings, and SAR disposition recommendations are exclusively authored and owned by human analysts. Automated models cannot write or modify primary case findings.
Second-Line QA Boundary (QMS)
QA reviewers evaluate closed cases against immutable point-in-time evidence. Reviewers cannot edit original case files, preventing circular audit tampering.
Rule & Model Tuning Boundary (TBT / HRS)
Tuned scenarios and ML models remain staging artifacts until formally approved by Model Risk Management and BSA Compliance Officers via documented audit workpapers.