Challenges and Opportunities in Data Mining in the Insurance Industry
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Data mining in the insurance industry is extremely important and crucial to the process of information gathering by companies who indemnify those dependent on accurate coverage, and who wish to improve customer communication and compliance issues. This article will outline how insurance companies can benefit from using modern data mining methodologies to reduce costs, increase profits, improve their CRM and CCM compliance, retain current customers, acquire new customers, and develop new products.
Data Mining Defined
To better understand the nature of data mining, it can be helpful to define the term. It can be known 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, through the application of comprehensive 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 mitigate customer attrition.
Recent Legislative Changes Affecting Data Mining
Recent U.S. federal legislation has cleared the way for changes in the way insurance firms can operate and compete in the United States and internationally. Although they have their roots in the Depression era, these legislative changes offer modern-day opportunities and challenges for those insurance firms that employ enabling technologies such as data mining to be more competitive in the growing global economy of the 21st century.
On November 12, 1999, U.S. President Clinton signed into law the Financial Services Modernization Act, which effectively repealed Depression-era financial legislation by enabling insurance companies, banks, and securities firms to affiliate with one another. Prior to that signing, the United States was one of only two major world economies with legislation prohibiting insurance companies, banks, and securities from offering each other’s products and services. The U.S. prohibitions were based on Depression-era judgements made about the causes of the Stock Market crash of 1929 and the ensuing economic woes. Those judgements led to the Glass-Steagall Act of 1933 and to the Bank Holding Company Act of 1956. These changes present significant challenges to, but also opportunities for insurance firms to improve their data mining strategies. The new challenges have been widely seen to have stemmed from:
Changes in distribution methods.
Implementing Data Mining Projects
There is a lot of talk regarding the best way to implement data mining projects in the insurance industry. Many authoritative books and shorter works that are written by IT experts cover the topic in detail. One message found in many of these works is that implementing a data mining project must consider real-world, practical challenges. A data-centric approach is especially effective and can be divided into the following functional areas:
Accessing the data.
Warehousing the data.
Analyzing the data.
Reporting the results of the analyses.
Exploiting the results for business advantage.
Reliable Access of the Data
Making sure you’re getting reliable, accurate data is a prerequisite for and foundation of proper data mining. A complete data access strategy should include the following key elements:
Access to all types of data sources.
Access to data sources regardless of their intrinsic platform.
Preservation of the source data through the adoption of security routines.
An easy-to-use, consistent GUI that, while not requiring an extensive knowledge of each data type, does provide the flexibility to meet specific needs.
Integration with the existing technology rather than access routines that require retooling of hardware and/or software or extensive, additional learning by users.
A properly designed and implemented data warehouse can help accomplish these key elements of a data access strategy.
A Sharp Focus on Customer Service
Many leading insurance companies are attempting to shift their focus away from the product-oriented models of the past and towards a more customer-centric policy to better serve their customers. Data mining technology can be utilized to better understand customers’ needs and desires. Analysis of marketing campaigns provides in-depth feedback and serves as the foundation of future campaign development.
Exploiting the Results for Business Advantage
The new information obtained from data mining can be incorporated into an executive information or online analytical processing and reporting system, and then disseminated as needed throughout the organization. The firm’s decision makers can then use the data mining results to answer important business-related questions such as, “How can we increase the ROI of our marketing campaigns?” for strategic planning and action. By exploiting data mining results in this manner, firms can better prepare for long-term growth and improve their opportunities for long-term prosperity.
The key to gaining a competitive advantage in the insurance industry is found in recognizing that customer databases, if properly managed, analyzed, and exploited, are unique, valuable corporate assets. Insurance firms can unlock the intelligence contained in their customer databases through modern data mining technology. Data mining uses predictive modeling, database segmentation, market basket analysis, and combinations thereof to more quickly answer crucial business questions with greater accuracy. New products can be developed and marketing strategies can be implemented enabling the insurance firm to transform a wealth of information into a wealth of predictability, stability, and profitability.
Have Questions About Data Mining Software Development?
If you would like help with setting up and using data mining technology and/or software, talk to a software support specialist at CSP, Inc, a leader in IT consulting and software support. Call us at (919) 424--2000 or email us at email@example.com to learn more.