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A leading FinTech company looking to leverage the power of Artificial Intelligence and Data Science



LUSIS DEC 18Lusis Logo

LUSIS is a leading French software and IT services provider that launched more than 15 years ago.  The company is well known for offering advanced software solutions to the global retail payment industry for critical online transaction processing but it also provides FX brokerage platforms and other trading related services. After launching a dedicated Artificial Intelligence department, it has now started to create AI based trading strategies. We asked Fabrice Daniel Head of the AI team at the firm to tell us more about these and some of the other FinTech solutions it now offers.

original source: November 2018 e-FOREX


What range of FinTech products and services does Lusis offer for Capital Market applications?

Lusis offers a wide range of FinTech products and services for Capital Markets:

Our product is based on a microservice architecture providing high capacity, high availability and great flexibility. We are hardware and middleware independent with the ability to support cloud-based deployments. As a company the work being done for the foreign exchange business will also spill over into our customers running on the Nonstop. This may be in the fraud detection space or even system monitoring with self-correcting rules and actions.


Lusis are experts in Artificial Intelligence and Machine Learning. How much impact do you expect these technologies to have in the FX trading environment?

We spent 5 years using data science approaches to analyzing and improving our trading platform. In June 2017 Lusis created a dedicated Artificial Intelligence department because we think AI will change end to end trading, automatic trading, portfolio management and it will impact everything that is related to Capital Market applications. We are especially focused on Deep Learning because of its incredible power and flexibility for problem resolutions.

Unlike the other Machine Learning approaches, for instance random forest, Deep Learning includes architecture as a concept. Today you are not just managing the number of neurons and layers of the model, and you don’t just select a feed forward, recurrent or convolutional model. Many approaches are now using more and more hybrid models mixing different types of Neural Networks to create more complex architectures with “building blocks” working together, each resolving a part of the problem.

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