Description: This article summarizes key points from the recently published research paper “Deep Learning for Liability-Driven Investment,” which applies reinforcement learning and deep learning techniques to liability-driven investment (LDI).

Description: Hear about some applications using quantile regression-derived individual and group claim distributions and how they may be used in various traditional health care modeling situations. The session will not focus on the technical use of quantile regression to obtain estimates of claim distributions, but more so is intended to focus on the uses of the distributions in actuarial modeling and the additive framework in assessing claims distributions. Presenters will show how actuaries can better assess risks by focusing on the individual- and group-specific claims distributions as opposed to traditional point-estimate-centric models.

Description: Career changes typically follow a major milestone—a promotion, a job offer, a new degree, maybe a cross-country move. For Patrick Getzen, exposure to some new perspectives ignited a shift.

Description: Hear about modernized reporting capabilities and technical analysis, which is enabled through access to deep actuarial and financial planning data. The webcast will focus on a real post-Long Duration Targeted Improvements (LDTI) source of earnings analysis and on a high-level LDTI forecasting capability.

Description: 2021 ImpACT Conference - Session 6E: Impact of New UW Data Sources and Tools 10.27.21 Recording

Description: 2023 ImpACT Conference Recording Bundle

Description: This article describes a method that can be useful when re-fitting a risk score model to a population of only moderate size. Learn how to use concepts like ridge regression to train a model with partial credibility.

Description: This is the strong message to encourage all actuaries in the world, who are struggling against low/zero/negative interest rate. Japanese actuaries have been struggling with low/zero/negative interest rate since 1995. There are experiences in asset management, product revision, pricing strategy, and capital management that can be applicable to other countries.

Description: The predictive analytics application growth in the Asia-Pacific region has been gaining momentum.

Description: Often actuarial practitioners are faced with working with data that is less than ideal. The data may be observed with gaps in it, a model may suggest variables that are observed at different frequencies, and sometimes predictive analytic results are very fragile to the inclusion or omission of just a few observations in the sample. Data, particularly big data, are often "messy" and something must be done about it. What is the actuary to do about these very practical matters? These types of questions are what this presentation is intended to address. Concerns on missing data, outliers, nonlinearity, mixed-frequency, cross-correlations are illustrated using real datasets in the Asia-Pacific countries. Some possible solutions to the messy data problems will be discussed.