Description: Recent research from Financial Finesse introduces a viable way to forecast the potential return on investment (ROI) of workplace financial wellness programs.

Description: Asset cash flow modeling and asset assumptions are growing in importance with PBR especially considering the low interest rate environment. The Academy’s Life Practice Council and Life Valuation Committee have formed a Low Interest Rate Asset Adequacy Testing Task Force which has conducted an industry survey to raise awareness and summarize the actuarial best practices for financial reporting actuaries practicing in today’s very low interest rate environment. As the scrutiny on the asset side ratchets up, actuaries must consider how best to develop the asset assumptions as well as model the reinvestment and disinvestment strategies. Coherent assumptions and modeling practices across modeling exercises are expected while respecting the purpose of each calculation.

Description: Claims are insurers deepest and richest transactional data – yet we rarely unleash the full potential of this data. In this session, we will discuss potential sources and attributes of claims data, traditional approaches alongside new analytic techniques and how to integrate models into existing claims management processes. During this session, the presenters will discuss different areas of how machine learning can drive significant value unleashing the potential in claims data, these include data capture, claims management and workflow design as well as behaviour prediction. In addition, the audiences will also have opportunity to learn different use cases of how claims models taking advantage of machine learning, such as foreign death fraud detection and critical illness triage.

Description: Develops a statistical procedure to fit bivariate distributions of claims in presence of covariates, which allows for a more accurate study of insureds' choice and size in the frequency and severity of the two types of claims.

Description: This presentation discusses a process for determining the minimum reinsurance retention level consistent with defined parameters. The conclusions reached were: 1. Under PDS dependence, optimal reinsurance strategy is multivariate stop loss form. 2. Explicit solutions could be derived in certain bivariate model. 3. Binding and unbinding constraints are equivalent. From Actuarial Research Clearing House, 2012, Vol. 1.

Description: This is a presentation on computing tight bounds for insurance payments with nonlinear risk. Semi-definite Programming is defined, some examples are presented, and non-linear applications are presented. From Actuarial Research Clearing House, 2012, Vol.1.

Description: This abstract describes a paper that proposes a Bayesian hierarchical model to analyze intercompany claim triangles.

Description: This tool created graphs of Google's mobility Data. This version of the tool contains global, country-level data through July 31, 2020

Description: Authored by a MIB Solutions team of Tom Rhodes, Stuart Klugman, Marianne Purushotham and Stacy Gill, the report summarizes the results of a study on the application and adoption of credibility theory within the life insurance and annuity industry. In addition to the report, numerical examples related to the application of credibility to company mortality and lapse data are provided in the four Excel files. This excel spreadsheet is a numerical example relating to the application of Credibility for lapses by policy for limited fluctuation. Please see the SOA website research section for the full report on Credibility Theory Practices.

Description: Amid the growing adoption of AI/ML models for underwriting and pricing, Life and Annuity carriers are facing increasing regulation in North America on Model Risk Management (MRM) and the use of AI. Colorado has set a precedent in the US by requiring reporting in June 2024, with other states looking to follow suit. To ensure compliance, Chief Risk Officers and Heads of MRM, working with their business partners, will need to evolve their MRM and AI risk frameworks, enhance model validation capabilities, and strengthen governance. This session will present: - Key trends and challenges faced by carriers amid increasing model landscape complexity and regulatory expectations - Regulatory outlook and implications for carriers' MRM and AI risk programs - An approach to enhance MRM and AI risk frameworks, including specific model validation program updates to address the unique risks of AI/ML models - Opportunities to enhance both MRM validation effectiveness and efficiency through the adoption of more risk-based approaches, smart sourcing, and automation - Case studies of how leading carriers are evolving their MRM and AI risk programs Upon attending the session, the attendees will develop a good understanding of increasing regulatory expectations, the required MRM and AI risk framework enhancements, and approaches to design and execute an effective MRM validation program in a cost-efficient manner.