Description: Actuarial models and modeling processes continue to grow in complexity, accelerated by the changes in U.S. Statutory, U.S. GAAP and IFRS reporting requirements in the past few years. Modeling and valuation teams are expanding their toolkits beyond the traditional vendor-maintained actuarial software and the MS Office suite of tools. Companies have started to use a variety of tools to improve model dev and valuation processes, including: - Data management - Automation and workflow - Reporting and analytics

Description: Stochastic modeling requirements are resulting in more 'run time' and less 'analysis time'. An expensive solution is leveraging cloud infrastructures and robust computing grids. The presenters will explore the use of cluster modeling and present case studies showcasing this automatable and robust technique, which could allow actuaries to recover the time lost to 'run time'. Developing automatable and clustered models requires robust governance practices to ensure credibility of results.

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: Discuss various analytics and use cases using payer published reimbursement contract data under federal mandate of Transparency in Coverage. These contract rates are publicly available in machine readable format (MRF) starting July 1, 2022, and refreshed monthly. By attending the session, you will have a better understanding of the state-of-art development and analytics using MRF data, including the technologies used to extract and analyze huge MRF files, validation and limitations of published rates, and their use cases.

Description: Modeling teams are getting larger to deal with more complex models and to support more enhancement requests from business and actuarial teams. Actuarial modeling teams can adapt tools and techniques used in the broader IT industry to be more efficient, handle large number of requests while maintaining their quality of work. Explore DevOps tools to track requests, progress made, work assignment and version control systems. See Azure DevOps in action as presenters use it as an illustrative tool to showcase these activities and how they can be beneficial to an actuarial modeling team (regardless of their preferred actuarial modeling system).

Description: "As digitization and automation rapidly progress in today’s life and health industry, issues with misrepresentation during the insurance application process are also becoming a growing concern for insurers around the globe. A recent survey finds that misrepresentation in application forms costs the average insurance customer an estimated 5% to 10% in higher premiums. In order to reduce this risk, insurers must find and correct areas of misrepresentation, consult post-issue sampling, review the automated underwriting processes, and monitor the quality of financial advisory firms. Learn how predictive disclosure modeling detects and reduces misrepresentation by harnessing readily available underwriting data to analyze issues, helping insurers understand various drivers for disclosure rates and areas of misrepresentation.Advantages of this modeling to various stakeholders in the life insurance journey include improved risk selection, claims experience, and reinsurance terms for insurers. For customers, it leads to increased certainty of coverage, payout of the claim, and ultimately lower premiums on average. By the end of the session, attendees will understand: The disclosure modeling process at a high level The technical details of a standard approach to disclosure modeling: data cleaning, standardization, visualization, and predictive modeling Success stories about how predictive disclosure modeling has impacted insurers"

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.