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Home > AI for Banking > Money Laundering Detection

Money Laundering Detection

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

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1. Get Inspired

Understand the Use-case under 5 minutes

2. Know More

Get to know more Business and Technical details about the use-case (15-30 minutes)

Deeper Intro

More detailed introduction covering business and technical aspects

Business Focused

Case studies, Organizational Aspects, Return on Investment examples

Tech focused

More details on the technical aspects of the use-case

3. Do

Technical resources that will help you implement the use-case (notebooks, tutorials..)

Data Sets

Data Sets you can use to build Demos, POCs, or test Algorithms

1999 Czech Financial Dataset

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

IBM AMLSim Example Dataset

This dataset is an example dataset generated from IBM AMLSim


Off-the-Shelf Products using AI for Money Laundering Detection

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