Description: Data science, predictive analytics and AI are certainly the buzzwords these days. They are heavily used and developed in underwriting and marketing but for most actuaries, they might/may as well be science fiction. The presenters will demonstrate how a normal actuary can utilize data science techniques in their daily job function without forming an official data science team within a company. It can be done in a gradual fashion so that normal business is not disrupted and improvements are made over time. The usage of data science techniques will include demonstrations of the following areas: - Regression analysis - Economic scenario generator - Experience study - Data quality checks - Inforce compression for explicit hedging

Description: The supplemental health market is changing. Carriers are looking to simplify claims processes, proactively identify missed claim opportunities, and reduce barriers to claim payment. As a result, more and more companies are looking at ways to use data from medical diagnostic and treatment codes. Take an introductory look at the most used medical codes—ICD (9 and 10), CPT, and HCPCS. Discuss the history and purpose of each code system and decipher the structure. Dive into how medical codes can be used in the supplemental health market (automatic claims adjudication, fraud identification, pricing), as well as common pitfalls encountered when using medical coding. By attending this session, you will have a basic understanding of the different types of medical codes, their actuarial applications, and common pitfalls.

Description: The famous 80/20 rule in data science states that only 20% of your time working with data is spent driving insightful business analytics. The rest of your time is spent trying to query, clean and organize your data. Even with this knowledge, the majority of actuarial training/focus is on the 20 percent. This session takes a different approach, focusing solely on speeding up your work on the other 80 percent. Practical examples will be part of a code walkthrough in R.

Description: The key tasks of credit risk capital project under CROSS II mainly focus onL(1 )Adjustment of measurement framework; (2) Calibration of risk factors, including both credit spread risk and credit default risk;3)Introduction of concentration risk measurement; (4) Higher requirement on look-through rule.

Description: CMS provides ACOs with monthly claims data for members, including their medical visits and ACO program eligibility. This data can be divided into Membership and Claims tables through the ETL process. However, when calculating PMPY at specific levels of detail using these tables, issues arise due to unrelated dimensions, leading to inaccurate and understated results. This article presents a solution to this problem by utilizing Tableau's data blending capabilities.

Description: In September 2017, the China Insurance Regulatory Commission (CIRC) issued the “C-ROSS Phase II project construction plan”, planned to be used across three years’ time, by perfecting the regulations, improving the operational mechanism and strengthening the regulatory cooperation.

Description: Open source programming languages have become a common part of the actuarial toolkit over the years. Using tools like R and Python, highly technical actuaries can automate onerous processes, easily work with massive amounts of data, create models and generate reports. Although these tools excel at turbo-charging analysts, it can be difficult for benefits to inure to the rest of the organization given the learning curve (“Can’t you just put this in Excel?”), control risks (“So your entire process could break overnight if some random person in Nebraska changes something?”), and internal IT environments. Listen in on a panel discussion where presenters share their personal stories of working with open source in a production setting. Hear about real world business problems, how open source tools were used, what risks were present, how they were overcome and the final business result.

Description: Session 9B: Actuaries and Technology: Perspectives on Actuarial Workflow Automation The life insurance industry has experienced unprecedented growth in modernization efforts, with special attention paid to automating actuarial workflows. Companies are creating processes that are more scalable and maintainable, helping actuaries meet strict deadlines and deliver meaningful information to stakeholders. Discuss practical examples of automation, potential compositions of the modern actuarial toolkit, and different ways actuaries and technology teams interact during implementation and production states. Examine how automation can help actuarial teams shift from a quarterly to a monthly valuation process by overcoming challenges in traditional actuarial processes-long run times, the need for highly specialized legacy software skills, and inadequate control. Shifting to a monthly schedule can increase alignment on company objectives, deepen insurers' understanding of their business, and improve strategic decision-making. Leave with new ways of thinking about technology, from day-to-day best practices to full-scale actuarial transformation. By attending the session, you will: - Explain and understand the benefits of automating actuarial workflows with the purpose of operating on a monthly, rather than quarterly basis. - Describe the categories and purposes of several kinds of automation and supporting tools that might be used to accomplish this. - Understand different actuarial IT operating models and consider the application of such strategies at your organization. - Identify best practices for implementing automation based on the presented case studies.

Description: Last year, the COVID pandemic significantly increased the pace of change in the life insurance industry, particularly in Accelerated Underwriting (AU). In the past, underwriting process changes occurred relatively infrequently and over a matter of years. Now, with new data sources and predictive models constantly evolving, the frequency of underwriting enhancements and decisioning changes, particularly in AU programs, is occurring every few months. With so much change occurring quickly, how can a company effectively monitor and understand the effects of these changes to make continuous adjustments with confidence and agility? Well-designed data visualizations allow a reviewer to navigate and comprehend detailed results quickly because our brains can process data in a picture format more than 15 times faster than text. Today’s advanced BI tools can make AU program monitoring more efficient and help companies adjust quickly to the results from program changes.

Description: Learn more about the predictive modeling that was applied to industry data for the lapse and mortality experience in the post-level term period. Novel approaches were taken to model the shock lapse, subsequent lapse, and mortality experience. Observe a demonstration of the Tableau tool, which was built to allow users to analyze data on their own.