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According to the California Department of Insurance, the Fraud Division received 15,112 suspected fraudulent claims (SFCs), assigned 532 new cases, made 201 arrests, and referred 317 cases to prosecuting authorities from 2020 to 2021. The potential loss amounted to $215,383,939. However, auto insurance fraud is underreported.

Insurance companies for auto are facing the headache of identifying the fraud out of the actual claims when it comes to automobile accidents. It requires analysts to dig deep into the client’s history and learn the behavior. It can also be acted by an identity theft or fraud scheme.


Using NEMESIS, analysts can identify claims that have a high probability of being fraudulent so that SIU investigators can analyze and start investigations. Using a data set of automobile insurance claims, users can build a predictive model to score claims. Without leaving the platform, users can create a model results dashboard for investigators to see trends, scores, and claim details for case management.


This case requires to use the 3rd-party data to link Personal Identification Information (PII) to claims. Use a dashboard to show cross-claim linkages as they appear with each new load of the 3rd-party data. The claim data includes policy information, insured person description, incident geo-location information, incident time, claim amount, auto model, etc.

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