Step by step guide for using Data mining in insurance industry

Companies in the Insurance industry collected enormous amounts of data about their clients. This is invaluable information about customers behaviour, activities, and preferences. To extract information from the whole amount of raw data Insurance firms lost time and efforts, due to their protective regulations. Let us see how data mining can be used in insurance industry.

1. The first step for an analyst is to select databases (data warehouses) that can be used for knowledge discovery.

2. After selecting an appropriate database for data mining it is necessary to set up the data mining software. The most suitable data mining method for fraud detection in insurance is Classification. Data mining software identifies groups of customers with distinct behaviour patterns.

3. The next step is “training” the data mining software. During this step the program will find decision rules, patterns, behaviours, trends and deviations from the norm.

After the knowledge discovery you will obtain decision rules or decision trees - a prediction model, containing structured features of insurance frauds or loyal clients. The decision rules might be used for predicting which customers or potential customers might commit fraud, and for further knowledge discovery.