Data Mining Proven Valuable to Prevent Fraudulent Insurance Claims

Data mining can lend considerable value to the organization seeking to leverage key business intelligence or predictive analytics to nurture key customer relationships. At the same time, this practice can also be used to detect insurance fraud and protect the organization from loss.

A recent IBM (News - Alert) white paper, “Using Data Mining to Detect Insurance Fraud, Improve Accuracy and Minimize Loss”, explores the significant loss insurance companies face every year as a result of fraudulent claims. Many of these claims are not caught simply because companies lack the necessary tools to easily identify which claims are legitimate and which may be fraudulent.

A number of insurance companies are now leveraging the IBM SPSS (News - Alert) data mining capability to ensure adjusters can target claims offering the greatest likelihood of adjustment. When IBM SPSS is introduced into adjuster investigating and auditing practices, fraudulent claims are more readily identified and eliminated.

The data mining tools provided in the IBM SPSS solution are based on industry standards that allow agencies to combine this mining with existing fraud detection and preventions. As a result, the insurance agency can increase accuracy, decrease manpower and minimize overall loss. The combined effort driven by IBM ensures optimal flexibility in the data mined and how results are deployed.

When implemented within the insurance adjuster process, data mining leverages a combination of powerful analytical techniques with firsthand business knowledge. This information is then turned into the insight necessary to identify probable instances of abuse or fraud.

As a pioneer in the data analytics field, SPSS offers IBM customers everywhere access to its leading-edge analytics tools to leverage true value from captured data through a unified platform that supports secure management and deployment of your analytical assets.

With the SPSS Modeler, you can combine powerful analytical techniques with existing fraud prevention and detection efforts; build models according to previously audited claims, using this data to identify potentially fraudulent claims in the future; ensure adjusters focus on claims least likely to be fraudulent; and deploy results that can be used to avoid fraudulent claims in the future.

Want to know more about the SPSS solution from IBM and what it can do with your data? Download this paper in full today. 



Susan J. Campbell is a contributing editor for TMCnet and has also written for eastbiz.com. To read more of Susan’s articles, please visit her columnist page.

Edited by Jennifer Russell