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Refine your detection methods and improve your fraud detection capabilities


Configure standard consumption patterns 


Refine your forecasts with multiple iterations defining an anticipated pattern


Detect abnormal behavior

With which tools?

  • With consumption and risk modeling

    With graphs showing actual consumption and the risk of fraud for a defined period. Visualize the evolution of the estimated consumption according to your initially defined parameters.

  • With a refined results

    Thanks to the numerous iterations carried out by our machine learning solution, we refine the understanding of the results and remove atypical values. Thus defining a level of consumption corresponding to a common behavior.

  • With the definition of confidence indexes

    Thanks to this scoring system, you can quantify and highlight sequences likely to contain fraud, whether for excessive or moderate consumption.

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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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