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Banking verified Verified Outcome TRL 9

Probabilistic Financial Crime Compliance (AML)

domain Client: A global systemically important bank (G-SIB) handshake Provider: Google Cloud schedule Deploy: 2023
95 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

To combat the inefficiency of legacy rules-based monitoring, a G-SIB migrated to a cloud-native, probabilistic AI model for Anti-Money Laundering (AML). The 'Dynamic Risk Assessment' system significantly reduced false positives while increasing the detection of genuine financial crime, validated by regulatory awards.

rate_review Analyst Verdict

"A definitive proof-point for the shift from deterministic to probabilistic compliance. The deployment demonstrates that cloud-native AI can satisfy rigorous regulatory standards while delivering order-of-magnitude operational improvements in a domain historically resistant to change."

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

warning The Challenge

Legacy rules-based AML systems were generating >95% false positives, creating a 'needle in a haystack' operational crisis. Compliance teams were overwhelmed by noise, spending vast resources adjudicating legitimate transactions rather than investigating complex criminal networks.

psychology The Solution

The bank deployed a 'Dynamic Risk Assessment' (DRA) utilizing machine learning on a cloud-native architecture. The system analyzes vast datasets to score customer risk behaviorally rather than via binary thresholds, running complex scenario modeling 16x faster than previous on-premise solutions.

settings_suggest Technical & Deployment Specs

Integrations
Core Banking, Transaction Monitoring
Deployment Model
SaaS / PaaS (Cloud Native)
Data Classification
PII / Transactional Data
Estimated TCO / ROI
Medium (Usage Based)
POC Summary (2022 to 2023)

"Pilot demonstrated ability to detect complex networks missed by legacy rules."

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Model Explainability Critical Implementation of Explainable AI (XAI) frameworks to satisfy regulatory audit requirements.

trending_up Impact Trajectory

Audited value realization curve

60% reduction in false positive alerts Verified Outcome
Primary KPI2x-4x uplift in suspicious activity detection
Audit CycleInvestigation time to detection reduced to 8 days

policy Compliance & Gov

  • Standards: Global AML/KYC Regulations
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: PII / Transactional Data

folder_shared Verified Assets

description
Verified Case Study
PDF • Version 1
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Technical Audit
PDF • Audited
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The "Blind Verification" Protocol

How we verified these outcomes for A global systemically important bank (G-SIB) without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-968
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-968

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