How data exchange can help us tackle insurance fraud and many more?

A way to move forward … A call for a cross-industry collaboration! 

Insurance fraud is a pervasive problem that costs insurance companies billions of dollars every year. It occurs when individuals or organisations make false claims to insurance companies to receive pay-outs they are not entitled to.  

Fraudulent insurance claims can come in various forms, including staged accidents, false medical bills, and fake injuries. Detecting and preventing insurance fraud is a complex challenge that requires advanced technology and sophisticated algorithms to identify patterns and anomalies in large amounts of data. 

Various AI technologies like; machine learning algorithms, predictive modelling, natural language processing and image recognition are being used by insurance companies to identify potential fraudulent activities and to detect suspicious patterns. 

Insurance companies usually decide to build their own fraud detection tools or outsource some parts of it to external technology vendors. However, these tools are only as good as the data they had been trained on.  I believe that in order to tackle this problem we need to shift our focus to an internal and external collaboration.  

The starting point, as well as the one, that makes one model better than the other, is the dataset that it is being trained on. The training process involves feeding the algorithm with input data and the expected output. The algorithm learns from the input data and adjusts its parameters to minimize the error between the expected output and the actual output. This process is repeated multiple times until the algorithm can make accurate predictions on new data.  

Gaining a full picture of the fraud patterns can only be achieved by sharing anonymised data across different markets, classes of business and teams. Two-way flow of data between the claims handlers and underwriting teams is key to identifying blind spots to a particular part of the risk and is also what should inform the risk appetite and pricing models.  

distriBind aims to become a global leading standards-free data exchange that enables a seamless real-time data flow between business partners. Our cutting-edge technological advancements, as well as the vision that it is based on, and the yet uncovered need of the market is what makes us a true Black Swan of insurance industry. 

Do you want to miss out on being a part of the DA 3.0 revolution?  

For more information, reach us out on contact@distribind.io and book a discovery call.