|Rosella Machine Intelligence & Data Mining|
Healthcare Fraud Detection
Fraudulent healthcare claims increase the burden to society. Therefore healthcare fraud detection is now becoming more and more important. Generally, healthcare frauds are not obvious and thus difficult to detect. The followings are typical examples of healthcare fraud techniques used by health care providers and patients;
Statistical healthcare fraud detection techniques
The net effect of excessive fraudulent claims is excessive billing amounts, higher per-patient costs, excessive per-doctor patients, higher per-patient tests, and so on. This excess can be identified using special analytical tools. Provider statistics include;
Analytic Healthcare Fraud Detection Methods
Healthcare fraud detection involves account auditing and detective investigation. Careful account auditing can reveal suspicious providers and policy holders. Ideally, it is best to audit all claims one-by-one carefully. However, auditing all claims is not feasible by any practical means. Furthermore, it's very difficult to audit providers without concrete smoking clues. A practical approach is to develop short lists for scrutiny and perform auditing on providers and patients in the short lists. Various analytic techniques can be employed in developing audit short lists. Keep in mind that excessive fraudulent claims lead deviations in aggregate claims statistics. In addition, fraudulent claims often develop into patterns that can be detected using predictive models!
Statistical listings of risky providers
When abusive claims are repeated frequently, the consequent is higher provider statistics. Various provider statistics can be used to identify fraudulent claims. For instance, audit short-lists may include the followings;
It is noted that statistical analytic techniques can reveal excessive providers who might be outright stupid! But it will be difficult to identify modest level fraud activities.
Predictive Modeling and Deep Learning
Predictive models can predict potential fraudulent claims and providers. Predictive models can be developed using information described above. However it is difficult to obtain training historical data as most fraudulent claims may have not been discovered!
(Predictive modeling tips) Large neural networks with a large number of input and hidden nodes lead to overfitting. Large networks, especially with small training datasets, can remember individual records. This is no good predictive model. A better way is to decompose problems into multiple smaller neural networks with a small number of input and hidden layer nodes, each network predicting one type of frauds. Then integerate them with higher level neural networks or use rules to combine them. For more on this deep learning style method, read Rule Engine with Machine Learning, Deep Learning, Neural Network.
Clustering can discover similar fraud cases!
Clustering can be used to find more fraud cases that are similar to known cases. The following figure shows CMSR Data Miner neural clustering. It has 9x9 (=81) cells. Each cell contains providers with "similar" attributes. Round figures are pie charts. Red portions represent fraudulent cases. Green areas are status-unknown portions. Providers within cells with red portions might be hidden cases that may need investigation;