The Discover AI Product Suite

Trigger-Based Tuning

TBT eliminates arbitrary rule threshold guessing in AML and fraud detection systems. By coupling historical alert efficacy data with out-of-time validation and operational capacity modeling, TBT provides financial crime teams with defensible scenario optimization, champion/challenger comparison, and full regulatory audit trails.

• Threshold Sensitivity Modeling: Simulate alert volume, conversion rate, and productive hit rate shifts across continuous and discrete parameter bands.
• Out-of-Time Back-Testing: Test proposed thresholds against historical customer populations across past quarters to prevent temporal overfitting.
• Capacity & Workload Forecasting: Forecast investigator FTE demand and SLA impacts directly from proposed parameter changes.
• Defensible Audit Workpapers: Automatically generate versioned model-risk governance workpapers suitable for internal audit and regulatory review.

High-Risk Scoring Lab

HRS Lab bridges the gap between modern machine learning and stringent regulatory model risk standards. Purpose-built for anti-money laundering and high-risk customer scoring, HRS Lab delivers reproducible feature engineering, temporal validation controls, explainable tree and ensemble architectures, and continuous post-deployment drift tracking.

• Point-in-Time Feature Engineering: Enforce strict temporal boundaries to prevent lookahead bias and target leakage during customer feature generation.
• Entity-Aware Cross-Validation: Partition training and test sets by distinct customer entities to prevent data contamination across related accounts.
• Explainable Attribution (SHAP & Factor Weights): Provide human-understandable factor contributions and risk drivers for every model scoring output.
• Continuous Drift & Population Stability Monitoring: Monitor Population Stability Index (PSI) and Characteristic Selectivity Index (CSI) post-deployment to detect concept drift.

Governed Data Engineering

Financial crime models and monitoring fail primarily because of broken data pipelines, silent schema drift, and data leakage. Data Lab provides governed financial crime data pipelines with automated data contracts, referential integrity testing, synthetic data generation, and temporal partitioning.

• Point-in-Time Dataset Construction: Reconstruct historical customer accounts, balances, and counterparty relationships as they existed at any specific millisecond in time.
• Automated Financial-Crime Data Contracts: Enforce strict schema validation, type checking, and boundary rules at ingestion to prevent silent pipeline corruption.
• Realistic Synthetic Data Engine: Generate high-fidelity, privacy-preserving synthetic transaction, entity, and narrative datasets for safe modeling and vendor evaluation.
• Referential & Typology Lineage Tracking: Trace every feature and derived metric back to source core banking, wire, ACH, and card feeds with cryptographic provenance.

Narrative Analytics & NLP Lab

Over 80% of critical investigative context is buried in free-text transaction memos, counterparty notes, and prior investigative narratives. NLP Lab applies governed NLP, entity extraction, and controlled LLM assistance to accelerate SAR drafting, detect evidence gaps, and ensure narrative consistency.

• Evidence-Grounded SAR Narrative Assistance: Generate structured investigative narrative sections where every sentence is tied to specific cited transaction IDs and account evidence.
• Typology & Predicate Crime Extraction: Classify unstructured transaction remarks into standardized FinCEN and FATF typologies (e.g., structuring, funnel accounts, trade-based laundering).
• Unsupported Claim & Hallucination Guardrails: Audit generated narrative drafts against raw case data to flag any claim lacking primary document backing.
• Sensitive PII Redaction & Prompt Governance: Sanitize narratives according to bank privacy policies and preserve prompt versioning for audit traceability.

Case Management System

CMS replaces fragmented spreadsheets and legacy ticket queues with an integrated financial crime workbench. Built for investigator efficiency and defensible auditability, CMS unifies alert triage, customer dossiers, transaction timelines, entity network visuals, and structured disposition workflows.

• Unified Customer & Entity 360: Aggregate historical alerts, account histories, transactional velocities, and beneficial ownership into a single pane of glass.
• Interactive Transaction Timeline & Filtering: Filter, slice, and visualize complex multi-account money movement across counterparties, corridors, and payment rails.
• Structured Evidence Dossier & Notes: Attach supporting bank statements, subpoena returns, and open-source intelligence with immutable cryptographic hashes.
• Configurable Multi-Tier Escalation & Segregation of Duties: Support Level 1 triage, Level 2 deep-dive investigation, and supervisory review with strict role segregation.

Quality Management System

QMS brings statistical rigor and audit defensibility to AML and fraud quality assurance. By automating review sampling, consistency checks between narrative and evidence, defect taxonomy tracking, and reviewer calibration, QMS ensures that investigative conclusions withstand regulatory scrutiny.

• Stratified & Risk-Based Review Sampling: Automate case sampling across high-risk typologies, newly onboarded investigators, and edge-case dispositions to optimize QA coverage.
• Evidence-to-Conclusion Consistency Verification: Verify whether documented findings in the narrative are supported by attached transaction receipts, KYC docs, and negative news.
• Standardized Defect Taxonomy & Severity Scoring: Categorize errors using standardized taxonomies (Technical Defect, Omission, Inadequate Investigation, Procedural) with clear severity weights.
• Reviewer Calibration & Inter-Rater Reliability: Measure Cohen’s Kappa and inter-rater agreement across QA reviewers using blind multi-review test cases.

Discover EntityGraph

Sophisticated illicit networks deliberately obscure ultimate beneficial ownership (UBO) through layered shell corporations, varied spellings, and surrogate accounts. Discover EntityGraph (DEG) delivers high-precision entity resolution, phonetic and multi-script name matching, and multi-hop network discovery.

• Probabilistic & Rule-Based Entity Resolution: Disambiguate matching names across millions of records using phonetic algorithms, address normalization, tax ID, and shared behavioral anchors.
• Multi-Hop Graph Network Traversal: Explore 1st, 2nd, and 3rd-degree relationships connecting counterparties, shared addresses, phone numbers, and authorized signers.
• Multi-Script & Transliteration Matching: Match entities across Latin, Cyrillic, Arabic, and Asian character sets with transparent match-component scoring.
• List Provenance & Differential Delta Updates: Track exact list versions (OFAC, EU, UN, PEP databases) with timestamped audit trails for every screening execution.