Data Mining Technology Helps Insurers Detect Health Care Fraud
Protecting financial assets from fraud or abuse is a universal and ongoing challenge facing the insurance industry. For those tasked with preventing, detecting, investigating and resolving these matters, data mining has become a key tool that maximizes both the effectiveness and efficiency of fraud investigations.
Data mining can be defined as the detection of relevant patterns, trends, relationships and correlations within a database. In the management of fraud and abuse issues, data mining techniques attempt to identify patterns within claims data that are different from patterns typically demonstrated by non-fraudulent individuals. Once the patterns have been identified, data analysis can take place. Data analysis is the process of analyzing mined data to interpret its significance.
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Data mining can take many forms and range dramatically in complexity and sophistication. In its simplest use, data mining can involve simple mathematics (addition, subtraction, multiplication and division) and be applied to extracts of summary claim information. These simplistic calculations can be performed by investigators using basic desktop applications and are best utilized in the investigative phase, once the subject of an investigation has been identified.
Basic data mining can be used by investigators to screen and prioritize a case, or even prove or disprove an allegation.
Advanced Query Tools
In more advanced applications, high-end software packages can be used in conjunction with a companys data warehouse to perform ad hoc data extracts and analyze the subsequent claims payment information.
As part of ongoing business operations, most companies maintain a claims database and a query tool to pull information from the database. Often, these query tools can be adapted or modified to identify common indicators of fraud and abuse. Since these applications typically depend on a formal query or rule set, they are well suited to perform high-frequency, high-volume analysis in a standardized manner. In addition, they do not require capital investments beyond whats needed for ongoing business operations.
Unfortunately, this solution does have its drawbacks. These applications typically require an advanced understanding of data architecture and query language. Also, because the knowledge of specific schemes and their indicators is a pre-requisite for developing an effective query, developing a library of rules to anticipate every situation can be a time-consuming and costly process. Even if one did have the resources to build such a library, the process would essentially be unending, as new fraud schemes continually emerge and evolve.