Description: This article includes a description of robotic process automation as it relates to actuarial departments. Robotic process automation is becoming more prevalent in actuarial departments as different regulatory changes cause the processes to be more complex. As robotic process automation takes over more jobs, actuaries will have to enhance and evolve their skillsets to better complement the new tools. This article provides an introduction for actuaries to inform them what they should expect, and how they can adapt to these changes.

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: This article will be focused on maximizing the reuse of code using features available within Moody’s Axis specifically targeted at using Formula Link.

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: This article introduces IT DevOps to actuaries and how it is applicable to them to improve their modeling efforts and make them more efficient.

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.

Description: The article discusses the use of R in prototyping automation procedures. It offers general pros and cons for R and walks through a specific use case, which involves scraping and summarizing data stored in Microsoft Excel.

Description: Everyone loves efficiency. But what does it truly mean for something to be efficient? An actuary who has spent almost 20 years managing process improvements shares his experiences and the evolution of his views on efficiency. This parable highlights the broader aspects of efficiency that are often missed, and some of the challenges that can come from targeting a typical view of efficiency.

Description: Producing a crystal-clear balance roll-forward on time, obtaining a fresh model output the first time or providing model results for your business partners with a tight turnaround all depend on one thing: how fast the model engine runs. Authors explored various reasons behind significant computational power consumption, and discussed different ways to speed the computational process such as in-force population compression, model use case consolidation, code simplification and streamlining, and infrastructure automation, etc.

Description: This article demonstrates how to implement DevOps for data using Moody's Axis. This was described as Model as Data (MAD). It described how extract turn-around documents from the model so they could be loaded back into the model. This feature allows continuous integration, continuous deployment and continuous testing of the model, which are important concepts in DevOps. This will allow the actuaries to speed up the model development cycle.