DISCOVER AI
Financial Crime Intelligence
DEGLifecycle: Detect · InvestigateAVAILABLE

Discover EntityGraph

Sanctions, Screening, and Entity Intelligence

Discover the entities, relationships, and connected risks behind every match.

Dedicated Sandbox Target:https://deg.discoveraisolution.com
The Operational Challenge

Why Financial Crime Teams Require DEG

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.

Quantitative focus: High-Precision Disambiguation & Hidden Network Discovery
Regulatory alignment: OCC 2011-12 & Federal Reserve SR 11-7
Primary User Roles:
Sanctions OfficersSenior InvestigatorsIntelligence AnalystsCDD/EDD Specialists
Governed Risk Domains:
Sanctions ScreeningPEP & Adverse MediaEntity ResolutionUltimate Beneficial Ownership (UBO)
Repository Verified

Verified Technical Capabilities

Every capability below is backed by working code, verified algorithms, and evidence sources in the engineering repository.

Probabilistic & Rule-Based Entity Resolution

VERIFIED

Disambiguate matching names across millions of records using phonetic algorithms, address normalization, tax ID, and shared behavioral anchors.

Evidence: Working/DEntity / src/entity_resolution.py

Multi-Hop Graph Network Traversal

VERIFIED

Explore 1st, 2nd, and 3rd-degree relationships connecting counterparties, shared addresses, phone numbers, and authorized signers.

Evidence: Working/DEntity / architecture_c4_and_flows.md

Multi-Script & Transliteration Matching

VERIFIED

Match entities across Latin, Cyrillic, Arabic, and Asian character sets with transparent match-component scoring.

Evidence: Working/DEntity / source_catalog_a1.md

List Provenance & Differential Delta Updates

VERIFIED

Track exact list versions (OFAC, EU, UN, PEP databases) with timestamped audit trails for every screening execution.

Evidence: Working/DEntity / data/source_registry_a1.csv
Operational Execution

Standard Operating Workflow

How DEG executes within regulated banking environments, with explicit human oversight roles at every phase.

01

Entity Ingestion & Normalization

Ingest customer, counterparty, and beneficial owner records, standardizing names, dates, addresses, and corporate registries.

Inputs / Outputs:
In: Core customer records, wire counterparties, sanctions feeds
Out: Canonical entity candidate store
Human Decision Role:
Data steward monitors data ingestion quality and feed updates
02

Screening & Multi-Attribute Scoring

Perform real-time and batch screening against global sanctions, PEP, and adverse media watchlists with transparent scoring.

Inputs / Outputs:
In: Entity candidates and active watchlists
Out: Potential matches with component breakdown (name, DOB, nationality)
Human Decision Role:
Screening analyst reviews match breakdown and confidence scores
03

Network Graph Expansion

Expand matching entities into relational graphs, uncovering shared attributes, common beneficial owners, and circular fund flows.

Inputs / Outputs:
In: Confirmed or flagged entity ID
Out: Interactive visual graph of connected entities and risk scores
Human Decision Role:
Investigator inspects multi-hop links and identifies surrogate entities
04

Disposition & False-Positive Whitelisting

Record defensible match dispositions with explicit evidentiary rationale and time-bound whitelisting controls.

Inputs / Outputs:
In: Investigator analysis and rationale
Out: Sealed audit record and updated entity resolution index
Human Decision Role:
Sanctions supervisor signs off on match clearances
Interactive Preview

DEG Operational Exhibit

Synthetic demonstration illustrating data representation, factor decomposition, and audit trail generation.

CAS-2026-09418Apex Logistics & Freight LLC(CUST-883019)
SYNTHETIC DATA ILLUSTRATIONPending L2 Supervisor Disposition
Primary Typology
Rapid Movement of Funds
Total Trigger Volume
$482,500.00
Review Window
Trailing 14 Days
Risk Rating
HIGH RISK
Txn ID
Timestamp
Type
Counterparty & Corridor
Amount
TXN-9011
2026-09-02 09:14:22
Incoming Wire
Mariner Shipping Corp (Cyprus) CY
$240,000.00
TXN-9012
2026-09-02 11:32:05
Outgoing ACH
Vanguard Holdings Ltd US
$78,500.00
TXN-9013
2026-09-02 13:05:40
Outgoing Wire
Kestrel Trading International PA
$161,500.00
TXN-9024
2026-09-05 14:20:11
Incoming Wire
Mariner Shipping Corp (Cyprus) CY
$242,500.00
Discover AI Synthetic Demonstration EnvironmentSchema Source: DataLab / Synthetic Fixtures v2

Governance & Defensibility

  • Deconstructs matches into explainable scores (e.g., 94% name match, exact DOB match, country conflict)
  • Never implies guaranteed detection of all illicit parties; documents boundary conditions and coverage limitations
  • Cryptographically logs exact watchlist versions and screening parameters for regulatory re-testing
  • Strict change controls around false-positive rules and customer whitelisting

Appropriate Use & Boundaries

Per Charter Section 11, Discover AI transparently discloses operational limitations:

  • •Resolution precision is constrained by the completeness of input identification fields (e.g., missing DOB or country)
  • •Screening efficacy depends on active, verified watchlist subscription feeds
Frequently Asked Questions

Technical & Compliance Inquiries

DEG can serve as a primary screening engine or as an intelligent second-line disambiguation and graph analytics layer that reduces false-positive backlogs by resolving customer entities before alert escalation.

Interoperable Suite Modules

CMSCase Management SystemHRS LabHigh-Risk Scoring LabData LabGoverned Data Engineering

Evaluate DEG in a Dedicated Synthetic Sandbox

Schedule an institutional technical review. We demonstrate Discover EntityGraph using synthetic data formatted to your exact core schemas.