The purpose of this page is to make it so that General Fraud Detection Strategy If you know who trick your organization and how they do that, then you know how you can perform effective fraud detection! There are no better ways than this! There are two steps you should follow to catch fraudulent claims; Find out "WHO" and "HOW" they trick your institution manually Healthcare frauds are normally from healthcare providers. By lodging bogus services, they cheat healthcare insurance companies. These claims and providers have common profiles and patterns. Identify profiles and patterns from known fraudulent cases manually. Find search rules Once you know profiles and patterns of fraudulent providers and claims, develop database search rules and procedures for them. It is noted that there are no standard advanced techniques for doing this. It's all about down-to-earth practical database searches that may involve programming exercises along with database queries. This is the down-to-earth method that should work well. The following sections describes exotic data mining techniques that may or may or may not applicable to your data. Healthcare Fraud Detection Techniques Healthcare fraud detection is now becoming more and more important. Fraudulent healthcare claims increase burden to society. They are generally difficult to detect. Here we describe how StarProbe data miner can be used to detect fraudulent providers and claims. In theory, it is possible to detect most frauds using data mining techniques. In practice, however, it is not easy to implement them successfully, as most industrial databases do not contain information needed for data mining techniques to be effective! If you think that you have proper information, contact us. Fraud Detection by Predictive Scoring (Indirect Method) Excessive fraudulent claims lead to far higher or far lower claims than average norms. If we can compute average norms as accurately as possible, it is possible to detect or isolate potential fraudulent providers. In another words, you may be able to identify them by identifying outliers. Projected norms can be computed based on the following information; Provider information: specialty, number of doctors, number patients, and other claims information. Geo-census data: area, population, major industry, average income level, and other geographic & demographic information. Base on this and the following information, we can build neural network predictive models that can compute expected values for the followings; Number of patients. Total amount claimed. Average patient cost.
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