Detect money laundering attempts by automatically spotting suspicious behaviours that are significant enough to qualify as high risk. Help compliance officers to focus on real suspicious transactions by reducing false alarms
Business value: prevent money laundering and financial crimes, gain customer trust, meet regulatory requirements, and avoid fines
Understand the Use-case under 5 minutes
Article (7 minutes)
Current state of money laundering in the world and the role of AI and machine learning play in reducing false positive alerts and detecting suspicious activities
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Article & Video (8 minutes)
SAS, a leading company in analytics and AI, is showing the effectiveness of using AI in money laundering detection using SAS Anti-Money Laundering
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Article (5 minutes)
The importance of money laundering detection for financial institutions, challenges of current anti-money laundering methods, and why to use AI and machine learning
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Get to know more Business and Technical details about the use-case (15-30 minutes)
More detailed introduction covering business and technical aspects
Video (1 hour)
A video by H2O.ai explaining details about money laundering detection techniques and providing an approach that can quickly and easily design models that will reduce false-positive alerts significantly while keeping low false-negatives rates
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Article (9 minutes)
The importance of using AI in detecting financial crimes, and how to make use of both humans and machines to fight money laundering.
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Case Study (10 minutes)
Bangkok Bank uses advanced analytics from SAS to meet expanding anti-money laundering requirements for global operations and ensure compliance keeps pace with dynamic regulatory frameworks
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Case Study (4 minutes)
SAS helps Landsbankinn identify suspicious transactions for additional streamline investigation, with 90% reduction in false positives.
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Case Study (3 minutes)
Discover how Ayasdi.AI was able to increase L3 discoveries by 120% for one of the world’s largest banks.
ReadMore details on the technical aspects of the use-case
White Paper (16 minutes)
Common AI techniques used in money laundering detection: anomaly detection, customer segmentation, customer risk ranking, and others - in addition to common challenges
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Article (5 minutes)
General introduction to money laundering and the different machine learning algorithms that can be used to detect money laundering activities.
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Technical resources that will help you implement the use-case (notebooks, tutorials..)
Github Repo
This project demonstrates end to end pipeline how to train binary anti money laundering (AML) classifier based on Generative Adversarial Networks (GANs) and Graph embeddings
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Kaggle Notebook
An implementation of Graph Convolutional Network to detect money laundering transactions
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A classification model that can detect suspicious companies based solely on publicly available data from the Companies website
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Tutorial (9 minutes)
This tutorial shows how to detect outliers or anomalies on unlabeled bank transactions with Python using an unsupervised machine learning algorithm called isolation forest
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Article (16 minutes)
AML using semi-supervised approach based on GANs, enabled by Hopsworks that scales to process huge datasets, and graph embeddings at scale, using Spark
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Research Paper (15 minutes)
A supervised machine learning method for discriminating between legitimate transactions and transactions that are suspicious in terms of money laundering
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Data Sets you can use to build Demos, POCs, or test Algorithms
Real anonymized Czech bank transactions, account info, and loan records released for PKDD'99 Discovery Challenge.
A multi-agent simulator of AML, sharing synthetically generated data so that researchers can design and implement their new algorithms over the unified data
This dataset is an example dataset generated from IBM AMLSim
Off-the-Shelf Products using AI for Money Laundering Detection
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