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Banking verified Verified Outcome TRL 9

Deep Learning for AML False Positive Reduction

domain Client: Major Nordic Bank handshake Provider: Teradata / Internal schedule Deploy: 2019-2023
89 Impact
Enterprise Ready
Evidence Score: 5/10
Strength: High

Executive Summary

ANALYST: COI RESEARCH

Following significant regulatory issues, the bank modernized its financial crime surveillance by deploying deep learning models to drastically reduce false positive rates in transaction monitoring.

rate_review Analyst Verdict

"A textbook recovery use case. By shifting from static rules (which flagged 99% innocent behavior) to probabilistic AI, the bank operationalized compliance without requiring an army of manual reviewers."

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

warning The Challenge

The bank faced a crisis of confidence and regulatory scrutiny regarding its anti-money laundering (AML) controls. Legacy rules-based monitoring systems were generating hundreds of thousands of alerts per day, with a false positive rate exceeding 99%. This volume overwhelmed compliance analysts, burying actual financial crime risks in a haystack of noise.

psychology The Solution

The institution implemented an advanced analytics platform utilizing deep learning algorithms. Unlike linear rules, these models analyze complex, non-linear relationships in transaction data to score risk probability. The system runs in parallel with legacy controls to validate results, prioritizing high-probability alerts for human investigation.

settings_suggest Technical & Deployment Specs

Integrations
Transaction Monitoring
Deployment Model
On-prem / Hybrid
Data Classification
Transaction
Estimated TCO / ROI
Medium
POC Summary ( to )

"N/A"

shield Risk Register & Mitigation

Risk Factor Severity Mitigation Strategy
Model Explainability High Model governance framework

trending_up Impact Trajectory

Audited value realization curve

60% reduction in false positives Verified Outcome
Primary KPIImproved detection of true positives
Audit CycleRegulatory audit clearance

policy Compliance & Gov

  • Standards: EU AML Directives
  • Maturity (TRL): 9
  • Evidence Score: 5/10
  • Data Class: Transaction

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 Major Nordic Bank without exposing sensitive IP or identities.

Private
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1. Raw Evidence

Audit ID: #PRIV-437
Evidence: Direct SQL Logs
Public
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2. Verified Asset

Outcome: Verified
Ref ID: #COI-437

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