Insurance Claims Data Analysis and Analytics

This Insurance Claims Data Analysis Dashboard includes Motor Insurance Claims Data based in the UK. The application runs on Qlik Sense Associative Engine which allows users to perform in depth analysis of the claims payments across a wide range of factors including time, location and claim type. In the application you can see how poor data quality and significant outliers can have a direct impact on the performance results of the company.

The Insurance Claims Analytics video below shows how you can use business intelligence to analyze insurance claims data to identify claims fraud, unusual transactions and data quality issues. You can try the Insurance Claims Data Analysis Dashboard yourself here in the demo page.

One of the issues insurance companies face is fraud. Fraud attempts have seen a drastic increase in recent years with the increase in online businesses thus making fraud detection more important than ever. Despite efforts on the part of the affected institutions, hundreds of millions of dollars are lost to fraud each year and quite likely to increase as well. Just like a needle in a haystack relatively few cases show fraud in a large population. Finding these is not just tricky but sometimes impossible too.

A key weapon for insurers in identifying these fraud perpetrators is the analysis of data. In a classical data analysis scenario, insurers need to be able to search for associations in data between similar types of claims, in similar locations, including something unique like a mobile phone number. These associations between the data can lead to a significant increase in identifying the groups of people that commit these types of fraud. This is exactly where a data visualization solution like Qlik Sense can play an important role in this activity.  Qlik Sense can help Insurance Fraud Analysts identify trends, patterns and examples of fraudulent Whiplash claims.

One step further can be predicting which claims are fraud cases using predictive analytics. Predictive analytics do not require insurers to go through the relationships in their data manually and try to find out the cases where fraud probability is high. This task can be tedious if there are many parameters in the claims data but can easily be handled by a predictive model.

For example below, you can see how an automated machine learning tool (Enhencer in the below case) can help to identify fraud cases.