Description: Healthcare cost is increasing at 9.3% in Asia in 2017 and health insurance cost will eventually catch up, making healthcare unaffordable to aging populations. Inefficiency in healthcare system is a contributing factor. In this session, use cases on how AI is impacting the health insurance operations on 3 core challenges. Document processing: A lot of documents are generated in the healthcare ecosystem, medical reports, diagnosis, invoices etc. Currently most of the documents are still reviewed manually and rely heavily on users’ judgement. Customer engagement: Customers are generally under distress and feel rejected during the claim process. It is due to the time and process needed to get the information they look for, the gap between the sales and claims process, and the reimbursement amount. Claim settlements: Claim adjudication is a technical process that review the disease, treatment and policy coverage. The process is specific to disease and difficult to be generalized. Thus, the process is far from consistent and invites fraud, abuse and wastage in this area.

Description: The insurance industry is in a constant state of transformation. As organizations adapt to the effects of COVID-19, undergo modernization efforts and implement new technologies, soft or “human” skills are becoming key differentiators for success. Skills such as communication, empathy, storytelling, leadership, problem-solving and more are separating the good from the great actuaries. Those who possess these skillsets can better translate data and most efficiently leverage systems and teams. At the same time, by taking a human approach to modernization efforts, leaders will be able to best engage and motivate their workforces, while building teams that will excel in the future of work.

Description: Machine learning models have many practical applications in the world of insurance. These applications are well documented and most actuaries, at least at a high level, are familiar with these concepts. The presenters will take this one step further and provide an introduction to the theoretical formulas and real-world considerations that make machine learning models possible.