STAFF_COMPANY_DETAIL.COMPANY_DETAIL

STAFF_JOB_HISTORY.COMPANY_NAME_LABELFRONTEO Inc.
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COMPANY_DETAILS.ADDRESS_OF_HQ
COMPANY_DETAILS.BUSINESS_TYPESTAFF_JOB_SEARCH.TRANSLATION_INTERPRETATION
COMPANY_DETAILS.FOUNDATION2003年08月07日
STAFF_JOB_SEARCH.WEBSITE https://www.fronteo.com/en/
BCAUSE.EMPLOYEE400
BCAUSE.CAPITAL ¥1,000,000
COMPANY_DETAILS.COMPANY_BUSINESS■ Legal Business: Discovery (International Litigation Countermeasures) Support Business / Forensic Research Business ■ Business Intelligence Business ■ Digital Marketing Business ■ Healthcare Business
COMPANY_DETAILS.COMPANY_INTRODUCTION■ Established August 8, 2003 ■ Number of employees Non-consolidated: 160 / Consolidated: 400 ■ Capital 2,507,346 thousand yen (as of March 31, 2018) ■ Average age 36.7 years old " Company characteristics "-Data analysis company utilizing behavioral information science" Behavior Informatics "- We use artificial intelligence to support international litigation and investigations of companies expanding globally. You will also be active in new businesses, digital marketing, healthcare, and business intelligence. The company has a track record of handling various data analysis projects such as litigation support and fraud investigations. And, by making the technology to discover evidence from a huge amount of electronic data extremely advanced in an environment where failure is not tolerated, it has become possible to perform unique big data analysis. This makes it possible for a person to infer "how to think", "what to do", etc. from the huge amount of electronic data. The company defines such efforts as "Behavior Informatics" and is attracting attention not only from the industry but also from experts and investors as "Future Discovery". " "service Details / Features "" [Features of KIBIT and the future] (1) You can do things you couldn't do before ■ Can be used by adding human judgment to a large amount of data: By learning the features contained in the teacher data given by the user and evaluating other data based on the learning result, the user's intention from the large amount of data You can extract the information that matches. ■ Low learning amount and realistic: When the amount of learning is insufficient, it can be relearned to improve its performance, so the maximum performance can be achieved with the minimum amount of learning. (2) Enrich "decision" ■ Understanding and proposing user preferences: By analyzing big data, you can recommend valuable information that users may not be aware of. "
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