Event – AI & BIG DATA EXPO – Virtual 2021

AI & BIG DATA EXPO – GLOBAL 2021 – Virtual event

LARUS, on March 2021, was silver sponsor of the
AI & Big Data Expo Global Virtual 2021: a virtual event for the Global AI & Big Data Ecosystem!

An online conference, consisting of live and on-demand sessions and covering two days of top-level content and thought leadership discussions for the ambitious enterprise technology professional, seeking to explore the latest innovations, implementations and strategies to drive businesses forward.

During the event were brought together forward thinking brands, market leaders, AI & Big Data evangelists and hot start-ups to explore and debate the advancements in Artificial Intelligence & Big Data, the impacts within Enterprise & Consumer sectors as well as Development platforms and Digital Transformation opportunities.

Topics covered included Business Intelligence, Deep Learning, Machine Learning, AI Algorithms, Data & Analytics, Virtual Assistants & Chatbots as well as case study based presentations, providing an insight into the deployment of AI across different verticals.

That’s the type of presentation that 
Alberto De Lazzari, Chief Scientist of LARUS, provided, together with Surya Josyula, Marketing Director of Fujitsu Laboratories of America.


In 2020, in fact, LARUS, with the partnership of Fujitsu Labs of Americas, launched on the market Galileo XAI, an insight graph data platform, based on the eXplainability of AI and will show you its application to improve car insurance fraud detection.

Using the solution powered by Fujitsu Deep Tensor, a leading Italian insurance company was able to automate a significant portion of the analysis process, leading to improved efficiencies and reduction in need for cumbersome manual analysis. 
The platform, moreover, enabled the anti-fraud team to focus only on relevant groups of subjects or accidents reducing the set of false positives.

Galileo XAI with its powerful graph visualization takes the explainability outputs given by Deep Tensor and shows them in a human understandable and friendly layout and format.

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