Video tracking applications are becoming commonplace with a growing number of use cases such as perimeter surveillance, asset tracking, medical imaging, and traffic violation monitoring. One of the problems that a developer must tackle while building a video tracking application is server overload.

Regardless of the many technological leaps made over the past decade, firms in the healthcare, insurance, and finance sectors still deal with a staggering amount of paperwork. Because of the lack of unified data platforms, it is still a common practice to use paper documents when there is a need for ad hoc data transfer between organizations. This is especially true in the case of insurance companies in India, which require you to attach medical records from hospitals with the claim request forms.

Claim adjudication, the process of determining the financial liability of a claim by the insurance company, is quite complex and time-consuming. Adjudication can be quick if the received claim is clear to the dot, in the sense that all the information is accurate and the claim is within the limits of the policy. But, as with all things in life, this is never the case. 

A large variety of fraud patterns combined with insufficient data on fraud makes insurance fraud detection a very challenging problem. Many algorithms are available today to classify fraudulent and genuine claims. To understand the various classification algorithms applied in fraud detection, I did a comparison using vehicle insurance claims data.