Description: Many retirement actuaries have experience developing, implementing, and monitoring Liability Driven Investing (LDI) strategies for plan sponsors. These strategies typically involve modifying a plan’s investment strategy to remove a significant portion of the investment and interest rate risk now or at some point in the future when specific triggers are met. The tactics often involve the use of fixed income assets to cash flow and/or duration match the liabilities. In the spirit of helping actuaries expand their roles in an evolving defined contribution world, this article explores the application of LDI and derisking strategies within defined contribution and individual retirement accounts. The economic theory behind this approach is called the lifecycle hypothesis, or lifecycle finance. The benefits of the lifecycle finance method are that it provides a safe approach to save for retirement and a secure source of floor income during retirement. Similar to pension plans, each individual has his or her own idiosyncratic needs, wants, and risks that should be considered. Actuaries are uniquely situated to help defined contribution plan sponsors, individuals, and even themselves implement the lifecycle finance approach by developing and distributing tools and methods that focus on income (rather than wealth), LDI strategies, and personalization.

Description: Discussion and Opinion on how randomness affects measurement of provider contracting risk-sharing agreements

Description: As the insurance industry continues to evolve due to a pandemic and rapidly changing economic and regulatory conditions, the role of data scientists and actuaries has become increasingly important. In an interview with the Atidot data science and actuarial team, we discuss what it is like to work at an InsurTech, how data scientists and actuaries can collaborate, and look at some concrete technical examples of where the innovation is having an impact. This article explores what collaboration can look like and how the role of data science and actuary may evolve in the InsurTech industry in the future.

Description: Introduces the concept of total risk analysis, a methodology used to answer questions like "what are the chances we will lose more than $1 million"

Description: Description and welcome to the 2020 Canadian Reinsurance Conference including highlight of the key note speaker

Description: A summary and analysis of REG-103163-18, the proposed regulations providing guidance for property-casualty loss reserve discounting (LRD) under IRC Section 846.

Description: High level overview on how to implement a risk adjustment chart chase program.

Description: Describes several regulatory changes that may impact the actuary working in a small company. These changes are not related to Principle Based Reserving.

Description: In the evolving realm of healthcare actuarial practice, value-based contracts are reshaping the landscape. This article explores the pivotal role of AI-enhanced Electronic Medical Records (EMRs) in empowering actuaries to navigate this dynamic sector. Despite challenges in data standardization, AI-enhanced EMRs offer a solution by extracting and standardizing diverse medical records. The article delineates how AI-EMR integration benefits actuaries and provider organizations, enabling more accurate forecasting, earlier shared savings prepayments, and real-time quality metric aggregation. Moreover, it discusses how this fusion addresses historical data limitations, can help project catastrophic claims, and can help identify risk score capture opportunities. Ultimately, it indicates the transformative potential of AI-EMR synergy, positioning actuaries at the forefront of healthcare analytics with unprecedented precision and insight.

Description: Spark offers a way to get some dramatic speed and scalability advantages over the Map-reduce methodology whenever you are doing iterative data processing and particularly when you want interactive data analysis capability. Find out why “lazy execution” can be a desirable characteristic when dealing with big data.