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A different approach:
We predict your next fraud case

~$1 trillion

Annual net written premiums in the United States


Yearly fraud estimation for USA only


Average cumulative Loss Ratio in 10 years for top 10 countries


Additional profit to insurance companies in top 10 countries due to 1% Loss Ratio reduction
  • The
  • Insurance companies are losing billions of dollars to Insurance Fraud annually.

    Existing legacy information systems struggle to catch this fraudulent activity.

    Risk evaluation is based on policy-centric approach.

    Time for a new approach.

  • The
  • Introducing getmeIns™.

    getmeIns™ takes best-practice Intelligence techniques to combat fraud.

    From a mass of noise and confusion, we distinguish intelligence clues.

  • Our
  • getmeIns™ delivers military-grade intelligence that detects and predicts fraud.


    brings a sophisticated and systematic approach to combating fraud by utilizing multiple disciplines such as Link Analysis, Open Source Intelligence, Visual Intelligence, Signal and Image Processing, Photogrammetry, Text Analytics and etc.

Complex Solution

Our fraud analytics relies on unique ontology of insurance processes built by our domain experts.

Unique ontology

With getmeIns™ insurance providers can access vital information ahead of time, preventing fraud, significantly reducing loss ratio, and building quotations that truly reflect their user’s behavior.

Possible fraud engagement score

Insurance fraud is always conducted by a group of people. The more human factor is involved in the procedure of getting a quote, underwriting, purchasing and claiming insurance, there is a greater chance for fraud. By combining link analysis with sentiment we evaluate potential fraud rings.

Identifying fraud rings

Today most insurance carriers are still using assumptions to generate quotes. In comprehensive car insurance,a 50 year old safe driver whose over-weight and suffering from arrhythmia would be traditionally considered a less risky policy holder than a 19 year old new driver. Our thesis proves the opposite.

From assumptions to real user behavior

We apply innovative photogrammetric algorithms to pictures taken by smart-phone cameras without using expensive hardware. Pictures are automatically matched against our database to prevent possible reuse of damaged parts in subsequent claims.

Image processing

We use text analytics software to process unstructured data, perform entity extraction and text classification.

Text Analytics


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A different approach:
We predict your next fraud case
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