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Detect suspicious activity across your transactions using Machine Learning


Configure rules for different behaviors 


Define risk scores and prioritize alerts


AI detects suspicious activity for you

With which tools?

  • With behavioral modeling

    Together with your business teams, we define rules to distinguish between "normal" and "suspicious" financial transactions, depending on the type of activity.

  • With prioritization of results

    Thanks to a scoring system, suspicious data is classified according to its degree of seriousness. This classification allows your teams to target the most risky cases.

  • With the definition of new fraud patterns

    Thanks to the statistical analysis of the data, our AI is able to detect new fraud patterns, not covered by the rules already in place. You can easily identify them, understand them and adapt your responses accordingly.

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Our publications


Boosting search engine capabilities of RegReview:…

RegReview is an AI solution for compliance teams, to automate regulatory monitoring and processes, which brings together several tools, the most essential of which is a search engine operating on a compiled database of custom-built regulatory sources.

The database contains ~300k documents.


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Article title

Labeling text clusters with keywords

We propose to explore several keyword extraction techniques to label text clusters obtained after a Text Clustering or a Topic Modeling pipeline. This work is following our previous articles about Topic Modeling and Text Clustering (here and here).


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Synthetic data or how to share sensitive data…

For a period of six months, 5 students from Centrale Supélec and ESSEC worked collaboratively with Sia Partners on building a Python library to create fake - which we'll call synthetic - data.
But what's the point of creating fake data? How could it help organizations?


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