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Risk & Compliance TRL TRL 9

Contextual Decision Intelligence for AML

domain Client: Global Tier-1 Universal Bank handshake Provider: Quantexa schedule Deploy: 18 Months
96 Impact
Enterprise Ready
Evidence Score: 9/10
Strength: Tier 1

Executive Summary

ANALYST: COI RESEARCH

Implementation of a graph-based data analytics platform to shift Anti-Money Laundering (AML) operations from static rules to contextual decisioning, eliminating data silos to create a dynamic single view of customer risk.

rate_review Analyst Verdict

"High-impact deployment addressing fundamental data silo issues; execution complexity lies in initial data integration rather than the platform capabilities. A benchmark for Tier-1 AML transformation."

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Full Audit Report Available Includes Risk Register, Technical Specs & Compliance Data.

warning The Challenge

Prior to this transformation, the bank's anti-money laundering operations relied heavily on legacy, rules-based transaction monitoring systems. These systems operated in isolation, generating an unmanageable volume of false positive alerts—often exceeding 95%—which required manual adjudication by thousands of investigators. This approach failed to identify sophisticated criminal networks that split transactions across multiple accounts and jurisdictions, leaving the institution exposed to significant regulatory penalties and reputational damage while consuming disproportionate operational resources.

psychology The Solution

The bank implemented a contextual decision intelligence platform powered by graph network analysis and dynamic entity resolution. Unlike linear transaction monitoring, this solution ingests data from internal systems and external registries to resolve distinct data points into a single customer view. It constructs complex network graphs in real-time to identify hidden relationships between entities. The architecture allows for dynamic risk scoring based on behavior and network associations rather than static thresholds, significantly improving the signal-to-noise ratio for investigators.

settings_suggest Technical & Deployment Specs

Integrations
Transaction Monitoring, KYC Systems, External Data Feeds
Deployment Model
Hybrid Cloud
Data Classification
Confidential (PII)
Estimated TCO / ROI
High initial CapEx, significant OpEx reduction long-term
POC Summary (2017-03-01 to 2017-09-01)

"Initial pilot focused on trade finance network analysis in UK/HK corridors."

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Data Privacy High Strict role-based access control and pseudonymization
Model Explainability Medium White-box model documentation

trending_up Impact Trajectory

Audited value realization curve

60% Reduction in False Positives Verified Outcome
Primary KPI2-4x Increase in Detection
Audit CycleBillions of Data Points Connected

policy Compliance & Gov

  • Standards: AML Directives (EU), BSA/AML (US)
  • Maturity (TRL): TRL 9
  • Evidence Score: 9/10
  • Data Class: Confidential (PII)

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 3
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Technical Audit
PDF • Audited
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Security Architecture

The "Blind Verification" Protocol

How we verified these outcomes for Global Tier-1 Universal Bank without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-330
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-330

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