Description: Underwriting is one of several areas in the insurance industry being impacted by insurtech. In this session, we will hear from experienced experts in underwriting transformation. Digitization, machine learning and other approaches are among topics to be discussed.

Description: The proposed session will introduce Outcome Based Risk Sharing Arrangements (“OBRSA”) between health insurance payers (“payers”, including insurance companies, health plans, sponsored employers, and government programs) and pharmaceutical manufacturers (“pharma”), and it’s intended to meet the needs of actuaries who focus on pharmacy benefits and risk sharing arrangements. The covered content will include (1) the background of OBRSAs, definitions, stakeholders, and OBRSA’s role in shifting the system from volume-based toward value-based; (2) conflicting perspectives about OBRSAs between payers and pharma (cultural and methodological); (3) identification of incentive gaps between payers and pharma, and incentive alignment between them; (4) barriers to implement OBRSAs; (5) limitations and considerations of OBRSA implementation; and (5) hypothetic OBRSA examples.

Description: "Visual storytelling is frequently used to effectively convey complex actuarial concepts to our clients and other audiences. When talking about data visualization, we often focus on the explanatory aspect. But data visualization is also a crucial tool in developing insights from large and complex data sets at every stage of data analytics spectrum: • descriptive (what happened?), • diagnostic (why it happened?), • predictive (what will happen?), • and prescriptive (how do I influence what will happen?). In exploratory and modeling stages of data analytics’ project, we let the data tell the story or explore our intuition and hypothesis through data pictures. We demonstrate the power of visual display in healthcare data analytics through vivid examples from the healthcare case studies. These illustrations of data visualization exploratory power range from assessing the quality of data or examining outliers, to exploring variables’ relationships and trends, to hypothesis testing, to guiding in feature selection and models’ comparison and evaluation, to model validation. Throughout these illustrations, we will discuss principles of graphical effectiveness, excellence and integrity."

Description: Natural Language Process (NLP) techniques can be used to find insights in large unstructured data sets and Word Embeddings are pervasive on a wide spectrum of NLP applications. These word representations improve downstream tasks such as Sentiment Analysis, Recommender System, and Topic Modeling. Learn how NLP transforms the Insurance Industry; and how word embeddings and pre-trained models improve the effectiveness and performance of NLP applications. NLP is increasing in importance for insurance companies. Additional application examples are available in session 36.

Description: Data is an essential component of financial analysis. In this session, the presenters will discuss data governance/management framework and how it can be applied to US GAAP LDTI and VM-51 regulations. This session will also discuss the important relationship between actuaries and IT and how they can work together towards best practices for extracting, managing and using data needed for various actuarial models. Real life case studies relevant for valuation actuaries (e.g. data needs for LDTI and VM-51) will also be presented and examined, highlighting best practices as well as challenges and pitfalls for setting up and governing data processes along with areas for where good communication and strong relationships are important to success.