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: 2011 Enterprise Risk Management Symposium, Chicago. This study investigates whether simulation-based models can be used to predict liquidity risk for banks, which must comply with Basel III by January 2015. The paper looks at the process of developing a measurement framework using Black-Scholes and Merton’s asset-based models to measure liquidity risk.

Description: The purpose of this paper is to provide a method for calculating special contingency reserves for investment losses. The method is derived by first building a stochastic investment model and then utilizing its probabilistic structure. From Transactions of Society of Actuaries 1980, Vol. 32.

Description: This paper provides a method for estimating long-tailed unpaid losses from paid loss development using trended generalized bondy development. From Actuarial Research Clearing House 1993, VOL. 3.

Description: The method described in this paper is to allocate surplus to each category of business and reserves in proportion to its estimated effect on an insurer's surplus variation. Risk load for each category of business is then selected so that the return on allocated surplus for each category is equal to the overall return on surplus. From Actuarial Research Clearing House 1993, VOL. 3.

Description: This article provides a brief overview of the current state of machine learning (ML) and artificial intelligence (AI) as they apply to actuarial science, including discussion around the integration of actuarial judgment with ML and AI. It concludes with an example of explainable ML through a python tool known as SHAP (SHapley Additive exPlanations), which is able to explain the key drivers behind individual ML or AI predictions.

Description: Market consistent valuations require path-dependent discounting for scenarios. IFRS 17's requirement to discount best-estimate cashflows using locked-in rates could give non-sensical results in many instances. This article describes a technique for calibrating each of the scenario's cashflows so that they can be discounted with other discount factors or curves without distorting the value of underlying options and guarantees.

Description: This chapter focuses on creating practical models that aid in the prediction of the risk of readmission to a specific hospital within 30 days of discharge for Medicare patients, to aid in the identification and stratification of at-risk patients for hospitals considering building an intervention program in a cost-effective manner. For readers who wish to develop their own predictive model, a dataset is provided on the Actexmadriver.com website, and R code is provided in the appendix to this chapter.

Description: This model arose from a project to evaluate the cost of a health care access' program to provide comprehensive medical care to uninsureds in Minnesota, for the Minnesota Health Care Access Commission. The project was to provide estimates of the impact on per-person and aggregate costs for various participation percentages in the access program. In order to evaluate those costs, the antiselection model described in this was developed. Of note, this was the ACTUARIAL EDUCATION AND RESEARCH FUND 1991 PRACTITIONERS AWARD WINNER. From Actuarial Research Clearing House 1991, VOL. 2.

Description: 2003 Valuation Actuary Symposium, San Diego, CA. In this Teaching Session the instructor discussed the importance of making sure the correct inferences were drawn from stochastic testing exercises. Topics included: 1. Applying the assumption of a normal distribution. 2. Drawing inferences about, and creating confidence intervals around, a population mean. 3. Drawing inferences about, and creating confidence intervals around, tail percentiles. 4. Certifying anticipated mortality under XXX in a mathematically concise way.