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Banking verified Verified Outcome TRL 9

AI-Driven Anti-Money Laundering Modernization

domain Client: Global Systemically Important Bank handshake Provider: Google Cloud schedule Deploy: 2019-2023
91 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: Tier 1

Executive Summary

ANALYST: COI RESEARCH

A global banking institution modernized its financial crime controls by transitioning from static, rules-based AML monitoring to behavior-driven machine learning models. The program focused on improving signal quality, reducing investigative noise, and strengthening enterprise-wide risk coverage under regulatory scrutiny.

rate_review Analyst Verdict

"A necessary evolution for large banks facing scale-driven compliance costs. Long-term success depends on sustained model governance and regulatory transparency rather than model accuracy alone."

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

warning The Challenge

Legacy AML platforms relied heavily on predefined rules that generated high volumes of false-positive alerts. Compliance teams were overwhelmed by low-value investigations, diverting attention from genuine financial crime risk. The operating model struggled to adapt to evolving typologies and increasing transaction volumes across regions and products.

psychology The Solution

The institution deployed machine learning models on cloud infrastructure to analyze behavioral and transactional patterns at scale. Risk scoring was continuously updated and integrated into existing case management workflows. Strong model risk management controls, explainability artifacts, and audit trails were embedded to satisfy regulatory and internal governance requirements.

settings_suggest Technical & Deployment Specs

Integrations
Transaction monitoring platforms
Deployment Model
Cloud
Data Classification
PII / Transaction
Estimated TCO / ROI
Not Disclosed
POC Summary ( to )

"Phased rollout across regions"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Model Explainability High Explainable AI frameworks and audit artifacts

trending_up Impact Trajectory

Audited value realization curve

Lower false-positive alert rates Verified Outcome
Primary KPIImproved detection yield
Audit CycleFaster investigation cycles

policy Compliance & Gov

  • Standards: Global AML / FATF
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: PII / Transaction

folder_shared Verified Assets

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Verified Case Study
PDF • Version 1
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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 Systemically Important Bank without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-366
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-366

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