This project develops a hybrid ensemble modelling approach that can help insurance companies combat fraud in more efficient ways. The key idea is to take advantage of both the business rules that are proven effective and the huge datasets that have now been accumulated in the insurance sector. Belief rule base (BRB) methodology is explored to develop a probabilistic rule-based model that can initially mimic and then fine-tune business rules established for fraud detection using labelled datasets. Pure data-driven machine learning (ML) models will also be developed, in order to detect fraud from different perspectives and to identify new fraud patterns from data. The two types of models: the BRB model and the ML model will be integrated by optimal training to develop a hybrid ensemble model and achieve the best accuracy and interpretability for fraud detection.
Ever since the introduction of insurance services in the mid-18th century fraud has existed, and it is now responsible for the loss of tens of billions of dollars each year. Increasingly, fraud detection services are seen to be a necessary component of a package of services offered by law firms. Identifying cases that should be disputed in the first place, is a key part of the process. Recent research has shown that computer algorithms can automatically search data and produce red flags that highlight cases where fraud is most likely. Most insurance companies already use automated red flags and business rules to assist in fraud prevention. Moving forward, this type of work can be done by more powerful AI decision systems, thereby making errors less likely.