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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.

How we do it.
1 Profiling
Profiling process starts with learning a real user behavior with integrated IoT mobile first platform. We generate unique user profiles based on personal activities, habits and lifestyle. Further we use open data sources as well as insurance carrier provided data sources to extract general sentiment and identify potential risk factors.
2 Analyzing
getmeIns™ platform is designed to process huge amounts of structured and unstructured data. Our analytical engine employs up-to-date graph database technologies to perform link analysis and identify possible fraud rings. We combine multiple database technologies both relational and No-SQL to perform data fusion. Specifically crafted photogrammetry algorithms allow us to process car damage photos taken with cell phone cameras without using special expensive equipment.
3 Scoring
Adaptive learning is used to create meaningful user profiles. getmeIns™ analytical engine generates risk score by combining user profile with OSINT (Open Source Intelligence) data and link analysis. The score is generated at point of sale when user purchases its first insurance product and it is constantly evaluated when new data arrives.
4 Alerts & Reports
Possible fraud signals such as behavior anomalies, participation in fraud ring, sentiment change and other alerts generated at point of sale are automatically streamed to the insurance carrier in real-time to defeat potential fraud.
5 Smart Quotes
Behavioral analytics in conjunction with our fraud prevention framework allows us to build smart quotes that truly reflect real user behavior. With our "Pay as you Live" philosophy we empower the users to customize their insurance coverage at the tap of a button and purchase insurance products according to their daily needs and changing lifestyles at most convenient price.

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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