Pattern Behaviour Prediction Using Data Mining in Insurance Application

Abstract

Gigabytes of data gathered by the health insurance sector have been analysed and mined to see if two data mining approaches are effective in uncovering previously undisclosed behavioural patterns. The act of choosing, examining, and modelling vast volumes of data to reveal previously unknown patterns is known as data mining. In the insurance sector, data mining may assist companies in gaining a competitive edge. The act of choosing, examining, and modelling vast volumes of data to reveal previously unknown patterns is known as data mining. In the insurance sector, data mining may assist companies in gaining a competitive edge. Both incident databases for diagnostic services as well as a general practitioner’s database were used in this study. The event database was analysed using association rules, and neural segmentation was used while overlaying the two datasets. It was shown that data mining in healthcare insurance information management may be used to identify trends in ordering pathology services and to categorise primary care doctors into groups based on the practise style and type. Conventional methods could not have produced the outcomes that were reached utilising the strategy employed.