STATISTICA Data Miner Predictive Modelling Solutions for the Insurance Industry

Life, Disability, Automotive, Health, Property and Casualty, etc.

Companies in the insurance industry are using STATISTICA Data Minerto be more effective and competitive in the utilization of historical data, using the latest predictive modelling and data mining approaches to recognize patterns within terabytes of data. STATISTICA Data Miner allows companies to predict trends in customers’ behaviours and responses, claims, and losses.

Major successes and savings have been achieved by companies using STATISTICA Data Miner for predictive modelling for rate making, fraud detection, and customer segmentation.

Areas of Application

Rate making

STATISTICA Data Miner identifies the most important root causes in the frequency and magnitude of historical losses. Predictive Models relating these primary factors to the frequency and magnitude of losses are then used to update rate tables accordingly, making the insurers more accurate and competitive in their policy rates when compared to more traditional rate making approaches. In the past, General Linear Models were the industry standard approach. Now, more effective prediction of losses is achieved through the use of predictive modelling techniques such as recursive partitioning (i.e., “tree methods“). 

Customer segmentation

STATISTICA Data Miner‘s Clustering module may be used for customer segmentation, by grouping the entire customer base into clusters, identified on the basis of various demographic and behavioural factors. These clusters can then be used for a variety of predictive modelling applications to determine the efficacy of the clusters in predicting outcomes of interest. 

Fraud detection

Claims fraud is a significant and costly concern, costing insurance companies several billion dollars annually. Losses due to fraud have increased dramatically in the past ten years. Despite actions by insurance companies, a large amount of fraud remains undetected.

STATISTICA Data Miner helps the insurance company anticipate and quickly detect fraud and take immediate action to minimize costs. Through the use of sophisticated data mining tools, millions of claims can be searched to spot patterns and detect even subtle variations in billing practices, by analyzing above normal payoffs along different factors like geographical region, agent, and insured party. 

Association Rule GraphSpecifically for health insurance, STATISTICA Data Miner‘s Associations Rules may be used to analyze claim forms. Using the Associations Rule module, the payer will be able to find relationships among medical procedures performed together, patterns in diagnoses and procedures across providers, etc.

PDF Insurance Fraud Detection Case Study

Claims analysis

STATISTICA Data Miner helps users understand subtle business trends in claims, which would have been otherwise difficult to spot.

STATISTICA Generalized Linear Models has the Tweedie distribution. This distribution is a flexible predictive modelling option. It can include exact zero and continuous data.

Predict which customers will buy new policies

STATISTICA Data Miner provides the insurance firm with reporting, tracking, and analysis tools to identify trends. Sequential pattern mining functions are powerful and can detect sets of customers associated with frequent buying patterns to inform future sales and marketing campaigns and tactics.

PDF STATISTICA Data Miner in the Insurance Industry, White Paper

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About statsoftsa

StatSoft, Inc. was founded in 1984 and is now one of the largest global providers of analytic software worldwide. StatSoft is also the largest manufacturer of enterprise-wide quality control and improvement software systems in the world, and the only company capable of supporting its QC products worldwide, with wholly owned subsidiaries in all major markets (StatSoft has 23 full-service offices, on all continents), and its software is available in more than 10 languages.

Posted on January 20, 2012, in Insurance and tagged , , , , , , , , , . Bookmark the permalink. Leave a comment.

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