In order for a business to thrive, besides finding ways to increase the income, cutting down expanses is also important. In some industries like Insurance, Financial and Healthcare, “Fraud, Waste and Abuse (FWA)” are considered as one of the top expanses which costs companies billions of dollars. Traditionally, this is handled by a group of expert and investigator to identify potential fraudulent cases, which is a time-consuming and labor-intensive process. With the rise of AI, there are more options to assist human to identify and prevent it from harming our community. This session will show not only “why” we should use AI to fight against FWA, but also the “how” it can be achieved practically by 3 major sections: (1) Supervised Learning: This part will explore how basic algorithm like decision tree, logistic regression and support vector machine can help in FWA and the corresponding result (2) Unsupervised Learning: This part builds on top of the previous section and will see how technique like clustering can complement the prior approach (3) Leveraging the latest finding of Generative Adversarial Network (GAN): That part will investigate the use of GAN, which is used to be employed in image generation, on FWA and see how this more sophisticated model can help fighting FWA. The session will include real examples with open data of which audience can find online. We will also include latest researches in this area to strength the cases presenting here.

Presenters:

Cupid Chan