Smart mechanisms for fraud detection in healthcare insurance

by | Nov 15, 2020 | 0 comments

Application Track:

Theme Driven



Proposed by:


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Summary of the entity:

List institute, a digital systems’ expert. Based in Saclay (Paris region, France), List Institute is one of CEA Tech three technological research institutes constituting CEA technological Division. Dedicated to smart digital systems, our mission is to achieve technological development of excellence for our industrial partners and create value, with more than 200 industrial collaborations per year. List institute counts more than 750 partners and every year more than a 200 partnership activities are being conducted with French and foreign industrial companies on applied research projects in four main topics: Advanced Manufacturing, Embedded systems, Data intelligence and Health ionizing radiations

Summary of the challenge:

The goal is to get innovative solutions for detecting different types of cases of health insurance fraud.


Stakeholders: Antifraud agencies


Antifraud agencies have historically used a brick-and-mortar approach to detect fraud, which involves manual inspection of individual cases already declared as suspicious in the value chain. This is, however, not cost-effective since the acts have already been committed and only 1% of fraud cases are detected (those with the largest amounts).

These agencies are therefore interested in innovative solutions for detecting different types of cases of health insurance fraud, based on the use of the most advanced big data technologies and high performance analytical and statistical algorithms. They should focus on the healthcare activities where healthcare professionals most frequently observe fraud: dentistry, eye care, hospital, and pharmacy reimbursements. Insurance companies are the clients of these agencies, which intervene by specific situations and types of fraud identified by the companies.

The challenge for antifraud agencies is to build a knowledge base of fraud operations, the data sources that can be used to detect.

Antifraud agencies are also interested in providing prevention of fraud services, by providing alert systems of potential fraudulent activities.


Expected outcomes:

  • To create a data value chain that allows antifraud agents to have the right data sources for early detection of fraudulent activities
  • To empower anti-fraud agencies with tools for data intelligence for fraud detection, while guaranteeing law compliance with respect to personal data protection and ethical issues

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