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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

51 sec

Overview of AI applications in banking

Doraly Chezeu Sukem Doraly Chezeu Sukem +1 · World Congress 2024

5:39 min

Two-step fraud detection with rules and machine learning

Doraly Chezeu Sukem Doraly Chezeu Sukem +1 · World Congress 2024

2:08 min

Utilizing artificial intelligence to scale financial asset classification

Henriette Brune Henriette Brune · World Congress 2026 Europe

4:45 min

Navigating technology adoption across business and IT functions

Rudi Bauer Rudi Bauer +1 · Cappuccino with HR

2:11 min

Securing heterogeneous legacy payment infrastructure against AI

Michele Zuccala Michele Zuccala +4 · World Congress 2026 Europe

2:53 min

Enhancing data validation and self-service analytics with AI

Kateřina Ščavnická Kateřina Ščavnická · World Congress 2025

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