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LARUS flies to Tokyo for the Neo4j Meetup!

Not for the cherry blossoms, but to speak at the Neo4j Tokyo Meetup, LARUS, with its partner Fujitsu Labs of America, flies to Japan!

Save the date: On Thursday, 22nd of April, Alberto De Lazzari, Chief Scientist at LARUS, together with Kanji Uchino, Senior Manager at Fujitsu Labs of America will give you an overview of Galileo XAI and its harnessing to Combat Fraud in Fintech and Insurtech Sectors.

The event will be in both Japanese and English languages, so don’t miss this fantastic opportunity to learn more about AI, and its explainability, applied to graphs and how it can help the Fintech and Insurtech business sectors!

This is for LARUS and Fujitsu Labs of America another important milestone to strengthen their partnership, this time in the Fujitsu home country.

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Talk Description

English Version
Fraud, especially its dynamic nature, is a major area of concern requiring significant time and resources to isolate from an enormous volume of transaction data. We have developed an innovative new composite AI based solution that combines graph-rule-based with graph-supervised-learning coupled with explainability to address this problem. Starting from our strong partnership, we’ll talk about LARUS solution GALILEO XAI, an insight graph data-platform based on eXplainable AI and powered by Fujitsu Deep Tensor. By exploiting the connectedness of data and extracting new indicators based on the structure of the graph, the solution enabled the anti-fraud team to focus only on relevant groups of subjects or entities reducing the set of false positives. Furthermore these indicators are seamlessly used by Deep Tensor to constantly improve the results.

Japanese Version:
金融や保険の詐欺、それらは特にダイナミックな性質なので、膨大な量の取引データから分離するためには、かなりの時間とリソースが必要になります。これは大きな問題です。私たちは、この問題を解決するために、グラフルールベースとグラフ教師付き学習を組み合わせた、説明可能で革新的な新しい複合AIベースのソリューションを開発しました。LARUSのソリューションであるGALILEO XAIは、富士通のDeep Tensorを活用したeXplainable AIベースのインサイト・グラフ・データ・プラットフォームです。両者は強力なパートナーシップを築いています。このソリューションは、データのつながりを利用し、グラフの構造に基づいて新しい指標を抽出することで、不正防止チームが関連するグループの対象者やエンティティのみに焦点を当て、誤検知を減らすことを可能にしました。さらに、これらの指標はDeep Tensorによってシームレスにつながり、常に結果を改善することができます。



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