Secure compute can allow you to build secure collaborative insurance fraud detection system without compromising any data.
In the United States alone, the total cost of non-health insurance fraud is estimated to be more than $40 billion per year, costing hundreds of dollars increase in annual premiums for legitimate users.
For example, there may not be a not simple way to check whether the same claim is filed with multiple companies. In many cases, an increased scale of shared data could lead to improved predictions and analysis for detecting insurance fraud. For example, an increased scale of claims data combined with other unstructured data will allow institutions to better identify potentially fraudulent insurance claims.
As a starting point, the data from multiple insurance providers could be linked to identify duplicate claims, filed against the same assets or the same incident across multiple insurers. However, much of this claim data is privacy sensitive (e.g., registration data, claims data, personal information, third party reports) and may not be easily shared among insurance companies due to privacy concerns.
Furthermore, insurance companies may not be willing to share such information with their competitors, since it could be leveraged to infer commercially sensitive information such as underwriting and pricing strategies. Therefore, using a privacy preserving solution, insurance companies can link their data to improve their fraud detection models without disclosing sensitive corporate or privacy sensitive customer information.
Our SecureCompute platform enables customer to securely build collaborative record linkage and machine learning models.
Read more on our white paper.