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WeAreDevelopers LIVE • Feb 17, 2021

Detecting Money Laundering with AI

Stefan Donsa , Lukas Alber

Traditional anti-money laundering systems generate 99.9% false positives. See how unsupervised machine learning and autoencoders completely transform compliance by boosting true-positive detection rates to 66%.

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#1 about 5 min

Transitioning artificial intelligence into operational business environments

Deploying machine learning models requires a comprehensive lifecycle covering prototyping, scaling, and continuous monitoring.

#2 about 4 min

Overcoming limitations of traditional anti-money laundering systems

Replacing legacy systems with machine learning reduces false positives and uncovers complex transaction schemes.

#3 about 3 min

Integrating business experts and conceptualizing analytical use cases

Combining compliance domain knowledge with varied data sources generates a holistic customer view for advanced modeling.

#4 about 4 min

Identifying suspicious transaction behavior via peer group comparisons

Evaluating transaction volumes against demographic peer groups enables models to flag distinct behavioral outliers.

#5 about 5 min

Applying dimensionality reduction for customer profile reconstruction

Compressing and rebuilding historical data properties reveals structural deviations that warrant compliance investigations.

#6 about 4 min

Comparing principal component analysis and autoencoders for reconstruction

Linear transformations and neural networks map master data attributes to isolate unpredictable customer behaviors.

#7 about 2 min

Evaluating unsupervised anomaly detection model performance in banking

Unsupervised machine learning solutions actively detect unmapped fraud patterns to drastically raise true positive alert rates.

#8 about 5 min

Building an anti-money laundering production architecture with Python

Data marts process internal pipelines by utilizing popular frameworks to orchestrate the complete machine learning workflow.

Matching moments

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Overview of AI applications in banking

Doraly Chezeu Sukem Doraly Chezeu Sukem +1 · WWC 2024

5:39 min

Two-step fraud detection with rules and machine learning

Doraly Chezeu Sukem Doraly Chezeu Sukem +1 · WWC 2024

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Utilizing artificial intelligence to scale financial asset classification

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2:11 min

Securing heterogeneous legacy payment infrastructure against AI

Michele Zuccala Michele Zuccala +4 · WWC Europe 2026

2:53 min

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Kateřina Ščavnická Kateřina Ščavnická · WWC 2025

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