Data mining can be defined as the process of selecting, exploring, and modeling large amounts of data to uncover
previously unknown patterns. In the insurance industry, data mining can help firms gain business advantage. For example, by applying data mining techniques, companies can fully exploit data about customers’ buying patterns and behavior and gain a greater understanding of customer motivations to help reduce fraud, anticipate resource demand, increase acquisition, and curb customer attrition.
This paper discusses how insurance companies can benefit by using modern data mining methodologies and thereby reduce costs, increase profits, retain current customers, acquire new customers, and develop new products.
Changes in Information Technology
As in other sectors of the economy, the insurance industry has experienced many changes in information technology
over the years. Advances in hardware, software, and networks have offered benefits, such as reduced costs and time of data processing and increased potential for profit, as well as new challenges particularly in the area of increased competition. Technological innovations, such as data mining and data warehousing, have greatly reduced the cost of storing, accessing, and processing data. Business questions that were previously impossible, impractical, or unprofitable to address due to the lack of data or the lack of processing capabilities can now be answered using data mining solutions. For example, a common business question is, �How can insurance firms retain their best customers?� Through data mining technology, insurance firms can tailor rates and services to meet the
customer�s needs, and, over time, more accurately correlate rates to the customer behaviors that increase exposure. Modern data mining technologies also offer more accurate and better performing models that are generated in less time than that with previous technologies. Graphical user interfaces (GUIs) enable more complex models, more
granularity of customer and product markets, and more sophisticated comparisons across methodologies. By generating better, extensively tested models in less time than was previously possible, insurance firms can more accurately address issues such as moral hazard in
underwriting and the adverse selection in marketing.
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