Description: Risk adjustment in government regulated health plans has been around for over two decades. Yet the strategies for promoting risk score accuracy haven't varied much during that time. Most health plans, physician groups, and vendors promote chart reviews and/or in-home assessments as the primary vehicle for collecting data not perceived to be present in the routine claims submission and adjudication stream. We know that most organizations trying to coalesce encounter data from multiple sources often fail to ensure that all relevant data is ultimately submitted to CMS or a state Medicaid agency. Crucial data, often with significant financial value, frequently gets lost in the data pipeline, somewhere between the provider's billing system and the health plan and/or its vendors. Because 837-formatted data is difficult to read, it complicates payers and vendor’s abilities to track encounter data so that every service delivered to patients is credited to the applicable risk adjustment formula. It is incumbent upon successful risk-adjusted entities to be able to track encounter data, identify processes and/or vendors which may be mangling or losing data, in order to ensure that risk scores are sufficiently accurate to be compliant with plan sponsor requirements and to ensure appropriate revenue to the health plan or provider group.

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: 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: Model runtime is a common challenge for actuaries in the life and annuity space. In particular, applications such as pricing and forecasting may require the use of nested-stochastic calculations, which may require extensive computing resources or use of rough approximations. There are also other areas where runtime may be a challenge, such as projecting certain reserves or layering solving algorithms within actuarial calculations. Recently, runtime challenges have been exacerbated by the development of increasingly complex insurance products and changes in reporting frameworks, such as IFRS 17, LDTI, PBR, and Solvency II. Many approaches have been explored by actuaries in the past to address model runtime challenges. To further this research, the SOA has recently published the paper Predictive Analytics and Machine Learning - Practical Applications for Actuarial Modeling. The authors of the paper will share their insights on innovative techniques using the implementation of artificial intelligence and machine learning (AIML). Come examine an introduction into AIML, review how AIML can be used to circumvent nested stochastic and other runtime challenges, explore case studies, review how costs and benefits can be measured and discuss model risk management considerations. Key learning objectives include: - Understanding the fundamental concepts of AIM - Understanding applications of AIML for actuarial modeling - Learning about the costs and benefits - Learning how AIML can be applied in practice to improve model runtime

Description: The frequent use and complexity of today's actuarial projection models has increased the importance of having efficient models. Model optimization can reduce both cloud costs and overall end-to-end clock times, thereby allowing companies to do more with less such as reducing close times, increasing controls, enhancing analysis, doing more complicated calculations, and/or improving employee workloads during the close. From design and data to execution and audit, come explore techniques that can be applied throughout the actuarial modeling process to optimize actuarial models for improved run-time, accuracy, and reliability. There will be a focus on techniques that don't rely on model compression. Topics covered include model design pitfalls, data handling, improving convergence on asset-liability projections, strategies for interacting with the cloud, and other tips for optimizing actuarial models.

Description: The frequent use and complexity of today's actuarial projection models have increased the importance of efficient models. Model optimization can reduce both cloud costs and overall end-to-end clock times. This, in turn, enables companies to do more with less, such as reducing close times, increasing controls, enhancing analysis, doing more complicated calculations, and/or improving employee workloads during the close. From design and data to execution and audit, join us to explore techniques that can be applied throughout the actuarial modeling process to optimize actuarial models for improved run-time, accuracy, and reliability. We will focus on techniques that don't rely on model compression.

Description: Models are a staple of an insurance company's operations. However, models that are not appropriately governed may lead to underpriced products, under-funded reserves, unfairly treated customers, and potentially catastrophic operational, legal, and reputational risks. Model Risk Management (MRM), an essential component of an insurer's ERM function, needs to keep pace with the rapidly evolving model ecosystem to limit model risk and produce results that management can trust when employing company strategy. With the adoption of ASOP #56, emerging technologies, and regulatory changes such as PBR, LDTI and IFRS 17, it is increasingly important for MRM frameworks and processes to adapt, upgrade and evolve. Discuss insights on how to embed ASOP 56 into Model Risk Management (MRM) and assess the design, development, modifications, and validation of actuarial models, the advancement of analytics and technology that empower insurers for process optimization and automation, as well as the the key components, stakeholders, best practices, and strategic benefits of a comprehensive MRM framework. Go over use cases on enhancing MRM in the context of ASOP 56, technology, and model governance. Explore how automation and optimization of the MRM process will allow companies to realize the strategic benefits of MRM, ensure successful adoption of regulation changes, and the pros and cons of emerging technology on MRM. By attending the session, you will be able to: - Describe how ASOP 56 can help improve MRM processes. - Describe the key components, stakeholders, best practices, and strategic benefits of a comprehensive MRM framework. - Identify where and how to use technology to automate and optimize the MRM process. - Evaluate the impacts of emerging technology on MRM.

Description: This is a discussion about computer models and simulation tools for life insurance companies. From Transactions of Society of Actuaries 1969, Vol. 21, No. 59AB

Description: Innovation in technology has disrupted nearly every industry, creating mounting internal and external pressures on organizations to accelerate the adoption of their digital agendas. The COVID-19 pandemic further brought to light the need for tools and technology to be adaptable, powerful, and scalable. As the insurance industry leaps into the future, actuaries must not only proactively refine their roles and responsibilities within insurance organizations but also seek opportunities for improvements and optimization in their day-to-day work. Discuss a variety of opportunities to streamline, accelerate, and optimize actuarial processes by using modern technology. Cover a wide spectrum of actuarial solutions from automation of actuarial data transformation to actuarial reserve calculations and calibration of economic scenarios. By attending the session, you will: Be able to articulate how modern low-code/no-code technology can help actuaries automate, streamline and optimize processes in yourday to day work tohelp focus on higher value tasks.

Description: The Society of Actuaries (SOA) Research Institute received a grant from the U.S. National Science Foundation (NSF) to educate insurance actuaries on the use of wildfire risk datasets and models to be developed by researchers from academia and the U.S. National Aeronautics and Space Administration (NASA).