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. 

Research shows that children primarily learn languages by observing patterns in the words they hear. Computer scientists are taking a similar approach to train computers to process human language. 

Imagine that you are working on machine translation or a similar Natural Language Processing (NLP) problem. Can you process the corpus as a whole? No. You will have to break it into sentences first and then into words. This process of splitting input corpus into smaller subunits is known as tokenization. The resulting units are tokens. For instance, when paragraphs are split into sentences, each sentence is a token. This is a fairly straightforward process in English but not so in Malayalam (and some other Indic languages).