Companies in the insurance industry are using STATISTICA Data Miner to be more effective and competitive in the utilization of historical data, using the latest predictive modeling and data mining approaches to recognize patterns within terabytes of data. STATISTICA Data Miner enables companies to predict trends in customers' behaviors and responses, claims, and losses.
Major successes and savings have been achieved by companies using STATISTICA Data Miner for predictive modeling for ratemaking, fraud detection, and customer segmentation.
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Areas of Application
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 ratemaking 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 modeling techniques such as recursive partitioning (i.e., "tree methods").
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 behavioral factors. These clusters can then be used for a variety of predictive modeling applications to determine the efficacy of the clusters in predicting outcomes of interest.
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.
Specifically 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.
You know there are fraudulent claims. Let's find them now.
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 modeling option. It can include exact zero and continuous data.
STATISTICA Data Miner, Fraud Detection White Paper
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.