Agenda Day Two

Brief summaries of progress and goals for the Predictive Analytics and Futurism section (PAF) plus a welcome to the Symposium and overview.

Session Coordinator(s)

Facilitator(s)

Presentation(s): View Presentation

Competency: Strategic Insight and Integration

Moderator(s): David L. Snell, ASA, MAAA

Presenter(s): Chuck Bloss, FSA, MAAA, FCA, SVP & Chief Actuary; Dr. Philip Smalley, MD, FRCPC, Chief Medical Director

Panelists will explore the emergence of data analytics within health care. Genetics/Genomics represent a high potential health medical advance. This session will cover how companies apply them to help the insured live longer with less morbidity and mortality, and to help insurers spend less on prescription drugs. Precision medicine can reduce the number of prescriptions necessary for covered participants, providing both better health results for the participant and lower costs for the health insurance provider.

Track: All

Session Coordinator(s)

Facilitator(s)

Presentation(s): View Presentation

Competency: Results-Oriented Solutions

Moderator(s): Dorothy Andrews, ASA, MAAA

Presenter(s): Jack T. Kerbeshian, FSA, MAAA

The Delphi method is a non-quantitative forecasting approach that is most useful when other forecasting techniques appear to have limited value. The Society of Actuaries (SOA) has had some impressive results from Delphi studies (even quoted in the Wall Street Journal for their uncanny accuracy on long-range predictions). Attend this session to learn about this effective, yet underutilized method of leveraging the wisdom of crowds.

Track: Manager/Supervisor 

Session Coordinator(s) Minyu Cao, FSA, CERA

Facilitator(s)

Competency: External Forces & Industry Knowledge

Moderator(s): Michael David Hoyer, FSA, MAAA

Presenter(s): Chris E. Stehno

This session will cover new data sources such as electronic health records, social media and the Internet of Things (IoT), and how they are dramatically changing the way we get our information for predictive analytics (PA).

Track: Beginner/Implementer 

Session Coordinator(s) David L. Snell, ASA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Relationship Management

Moderator(s): Bradley Joseph Lipic

Presenter(s): Eileen Sheila Burns, FSA, MAAA; Jeevan Duggempudi; Qichun Xu, FSA

This session will cover building a Data Science team including hiring and retaining talent, and bringing different skillsets to the table such as Data Engineers, Data Scientists and Data Storytellers. Where actuaries fit into this picture, how actuaries work with data scientists and how to get a job in this new area will also be discussed.

Track: Advanced Practitioner

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Michael Cletus Niemerg, FSA, MAAA

Presenter(s): Talex Diede

This session will cover clustering techniques generally (k-means, hierarchical), and how k-means have been used to segment policyholders using third party features such as credit scores to assess customer profitability.

Track: All

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Kimberly M. Steiner, FSA, MAAA

Presenter(s): Julia Romero, FSA, CERA

This topic will cover how to integrate traditional actuarial profit models with data science/predictive models, so one could use traditional data science techniques and modeling approaches to evaluating profitability and customer value. This session will cover rethinking actuarial profit models as an end-to-end model in a data oriented world.  

Track:  All

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Jason Hiquet FSA,CERA

Presenter(s): Marjorie A. Rosenberg, FSA

This session will cover a case study to illustrate a novel way of clustering individuals to create similar groups where covariates are all categorical. This method adapts to categorical data where there is no inherent order in the variable, like race. Data from the National Health Interview Survey (NHIS) was used to form the clusters and apply them for prediction purposes to the Medical Expenditures Panel Study (MEPS). This approach considers the person-weighting of surveys to produce clusters and expenditure estimates per cluster that are representative of the US adult civilian non-institutionalized population. For the clustering method, we apply the K-Medoids approach with an adapted version of the Goodall dissimilarity index. This approach is validated on independent NHIS/MEPS data from a different panel. The results indicate the robustness of the clusters across years and indicate the ability to distinguish them for the predictability of expenditures.

Track:  Beginner/Implementer

Session Coordinator(s) Stuart Klugman, FSA, CERA

Facilitator(s)

Presentation(s): View Presentation

Competency: Strategic Insight and Integration

Moderator(s): Stuart Klugman, FSA, CERA, Ph.D.

Presenter(s): Chris E. Stehno; Francois Millard, FSA, FIA, MAAA

Two signature issues today are the big data and behavioral science revolutions, however they are seldom discussed together. This session will present a framework in which data science and behavioral (“nudge”) science function as two parts of a greater whole. Behavioral science provides an expanded toolset for operationalizing predictive models; it also serves as a wealth of ideas for getting the most out of big digital technology to create innovative, pro-social products and business models. This presentation will sketch basic principles of behavioral economics and choice architecture, and provide examples of innovative data science/behavioral science mash-ups.

Track: Manager/Supervisor

Session Coordinator(s) Xiaojie Wang, FSA, CERA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Dennis Kapylou, ASA

Presenter(s): Kyle Nobbe, FSA, MAAA; Vikram Kamath

For many, predictive modeling is simply feeding data into a “black box” and getting output. However, understanding the model development process is critical when it comes to validating model results and implementing learnings in a business setting. This session will cover the process of building a simple model then demonstrating key ways actuaries should validate results.

Track: Beginner/Implementer 

 

Session Coordinator(s) Rosmery Cruz

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Rosmery Cruz

Presenter(s): Jeff T. Heaton, Ph.D.

Bring your laptop to experience this deep learning neural network tool offered free from Google.

Track: Advanced Practitioner

Session Coordinator(s) Rosmery Cruz

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Randal Olson, Ph.D.

Presenter(s): Benjamin Copeland, ASA, MAAA; Joseph Charles Long

The growth of huge social networks like Facebook, Twitter and LinkedIn have sparked innovation in graph analytics. Graph analytics is not about data visualization; it is instead about modeling the connections between people. Graph analytics is used to suggest new friends to connect to and what stories you might want to read in these social networks. These same analytic frameworks can be used for novel applications in health care. For example, inferred referral patterns can be used to build a social network graph of health care providers. From there, interesting visualizations and inferences around network leakage or post-acute care can be generated. This session will introduce the concepts behind social network analysis and provide case studies of its application to health care.

Track:  All

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Jason Hiquet FSA,CERA

Presenter(s): Boyi Xie

When we talk about data, one usually thinks of numerical data in a tabular format, however, the development of text mining techniques made it possible for the analyses on unstructured, textual data. Natural Language Processing is a field in Artificial Intelligence (AI) that focuses on data relevant to human languages, such as words and documents. In this session, we will introduce the development of Natural Language Processing, and its application to the insurance industry, such as information extraction from submission documents, identifying events and trends from news articles and classifying lawsuits into loss scenarios for experience studies.

Track: Beginner/Implementer 

Session Coordinator(s) Xiaojie Wang, FSA, CERA

Facilitator(s)

Presentation(s): View Presentation

Competency: Strategic Insight and Integration

Moderator(s): Taylor Fay

Presenter(s): Rajiv Shah

This session focuses on how automated artificial intelligence (AI) and machine learning (ML) are transforming the insurance industry. Through case studies, this session will cover how AI is able to understand otherwise unknown processes better, make it transparent and increase insurability (e.g. quantifying mortality risk of customers with complex medical history or insuring high-frequency claimants through granular risk segmentation). We will also discuss why model transparency and validations are critical components of AI and ML workflow - for both the success of the insurers as well as the well-being of the consumers.

Track: Manager/Supervisor 

Session Coordinator(s) David L. Snell, ASA, MAAA

Facilitator(s)

Join your fellow meeting attendees at this networking lunch. Catch up with colleagues and friends, discuss previous sessions and make new acquaintances.

Session Coordinator(s)

Facilitator(s)

Competency: Technical Skills & Analytical Problem Solving

Session Coordinator(s)

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Jeff T. Heaton, Ph.D.

Presenter(s): Dihui Lai, ASA

The world has witnessed great advances in machine learning (ML)/artificial intelligence (AI) technologies. However, most AI systems are developed for general purposes (e.g. face recognition, language translation, voice recognition etc.). AI designed for insurance is less common. This session will demonstrate an AI system customized specifically for underwriting purposes and how it improves the efficiency of underwriting workflow.

Track: Beginner/Implementer

Session Coordinator(s) Rosmery Cruz

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Gary A Hatfield FSA, MAAA, CERA

Presenter(s): Geoffrey R. Hileman, FSA, MAAA; Norman M. Storwick, FSA, MAAA

Fuzzy logic is conceptually interesting but can it add value? This session will review potential uses for fuzzy logic and how it can improve actual operations. At the end of this overview, we will demonstrate an actual solution and compare the value of fuzzy logic to other analytic approaches.

Track: Advanced Practitioner

Session Coordinator(s) Xiaojie Wang, FSA, CERA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Dennis Kapylou, ASA

Presenter(s): Chris Wells

Python has become the most popular programming language among data scientists while having popularity in many other disciplines as well. Attend this session to learn how to get started in this amazingly versatile language.

Track: Beginner/Implementer

Session Coordinator(s) Kevin J. Pledge, FSA, FIA

Facilitator(s)

Competency: External Forces & Industry Knowledge, Results-Oriented Solutions

Moderator(s): Michael Cletus Niemerg, FSA, MAAA

Presenter(s): Gershon Henoch Firestone, FSA

Digitilization of Underwriting has three primary impacts: 1) changing the way business is done due to automation; 2) better understanding of the business; 3) change the way data is used. This session will cover some or all of the following: the process of impact of the three listed above, case studies and the comparison of data available today and in the future and how data utilization will be impacted by tomorrow's data.

Track: Manager/Supervisor 

Session Coordinator(s) Xiaojie Wang, FSA, CERA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Michael Blakeney, FSA, MAAA

Presenter(s): Rosmery Cruz

Simple mathematical equations outperform very complex ones in predictive analytics (PA) situations. This session will cover why and how to guard against that and other items that might not be intuitive.

Track: Beginner/Implementer 

Session Coordinator(s) Rosmery Cruz

Facilitator(s)

Competency: Results-Oriented Solutions

Moderator(s): Anthony Cappelletti, FSA, FCAS, FCIA

Presenter(s): Boyi Xie

This session will present a predictive analytics (PA) framework for a professional liability portfolio. The business line is facing two challenges: 1) the underwriting procedure is complicated – underwriters need to look at many underwriter triggers that have little indication of future risk; 2) the portfolio loses clients because of an over-complicated application process, (e.g. more than five pages of forms). We rely on predictive analytics (PA) to develop an accelerated underwriting process to better quantify risk. It will help refine underwriting triggers to improve efficiency so underwriters can touch little on low risk policies. We also aim to simplify the application process by asking minimum questions while maintaining accurate risk estimation.

Track: Manager/Supervisor

Session Coordinator(s) Xiaojie Wang, FSA, CERA

Facilitator(s)

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Anders Larson, FSA, MAAA

Presenter(s): Qichun Xu, FSA

This session will cover how with an education background and business experience, an actuary can quickly learn the basics of data science and perform the important modeling work in insurance.

Track: Beginner/Implementer 

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Presenter(s): Yuanjin Liu, ASA

The language R is terse, powerful, free and able to run on several different machine platforms and operating systems. Fluency in R may soon become a selection factor in getting your next job. This session will demonstrate some of the predictive power you can have beyond spreadsheets. It will cover how to get started in the new world of predictive analytics (PA). This session will be a beginner-level tutorial of how to fit, validate and interpret a logistic regression model with R.

Track: Beginner/Implementer 

Session Coordinator(s) David L. Snell, ASA, MAAA

Facilitator(s)

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Michael Blakeney, FSA, MAAA

Presenter(s): Adnan Mohammed Haque

A number of different companies have explored different statistical and machine learning techniques for looking at their mortality experience. What have they learned? How did they choose which tools to use and how do they validate this approach vs. the traditional methods of the past?

Track: Beginner/Implementer 

Session Coordinator(s) David L. Snell, ASA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Professional Values

Moderator(s): Jordan Durlester

Presenter(s): Bradley Joseph Lipic

This session will discuss strategy and tactics for becoming good stewards of consumer/customer data. Topics will include various techniques, such as anonymization, pseudonymization, tokenization and the merging of disparate, de-identified data source.

Track: Manager/Supervisor 

Session Coordinator(s) Rosmery Cruz

Facilitator(s)

Presentation(s): View Presentation

Competency: Results-Oriented Solutions

Moderator(s): Rosmery Cruz

Presenter(s): Jeff T. Heaton, Ph.D.

The human race is great at devising many different ways to accomplish the same task; predictive modeling is no different. Data scientists can choose from a wide variety of languages to prepare data, fit models and verify results. The Rosetta Stone, which showed writing composed in three languages side by side, was instrumental in translating unfamiliar Egyptian hieroglyphics into the more common Greek language. This presentation follows a similar approach by showing the same predictive model implemented in the following languages: R, Python, SQL, Excel, MatLab, SAS and Julia. Attendees will compare and contrast each of these languages andbuild a greater understanding of those languages less familiar to them.

Track: Advanced Practitioner

Session Coordinator(s) Rosmery Cruz

Facilitator(s)

Presentation(s): View Presentation

Competency: Results-Oriented Solutions

Moderator(s): Dorothy Andrews, ASA, MAAA

Presenter(s): Cheng-Sheng Peter Wu, ASA, FCAS, MAAA

This session will discuss how insurers are applying predictive analytics (PA) to improve their business which will include examples from general Insurance such as auto, home and umbrella.

Track: Manager/Supervisor 

Session Coordinator(s) David L. Snell, ASA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Anders Larson, FSA, MAAA

Presenter(s): Michael Cletus Niemerg, FSA, MAAA

Interpreting sophisticated models like GBMs or Neural Networks can be a daunting task. This session will focus on ways to help interpret and understand the output of complex models.

Track: Beginner/Implementer 

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Kimberly M. Steiner, FSA, MAAA

Presenter(s): Nicholas Scott Hanewinckel, FSA, CERA

This session will cover interactive demonstrations of how to get started (or how to move to the next level) in predictive modeling.

Track: Beginner/Implementer 

Session Coordinator(s) David L. Snell, ASA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Jason Hiquet FSA,CERA

Presenter(s): Rajiv Shah

Automation of predictive analytics (PA) is changing analytics. This workshop lets the audience try automated analytics. The effects on accuracy, transparency and lowering the barriers for analytic usage will be seen. The presenter will also share how organizations are responding and transforming around automated analytics. 

Track: Beginner/Implementer 

Session Coordinator(s) Xiaojie Wang, FSA, CERA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Erica Rode, ASA, MAAA

Presenter(s): Sheamus Kee Parkes, FSA, MAAA

The cloud inspires awe and fear in equal parts these days; there is a need to learn more, but there’s much information to learn and it may be difficult to know what to learn next. In this session, we’ll carve off a small corner of the cloud and make it your own. Setting up a Data Science Virtual Machine on Digital Ocean will be demonstrated. It’s the fastest way to get a data science development environment setup for your own use. Many of the topics and ideas covered will apply to almost all other cloud offerings. What use cases Digital Ocean is good for and what use cases you should look elsewhere for will also be discussed.

Track: Beginner/Implementer 

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Results-Oriented Solutions

Moderator(s): Rosmery Cruz

Presenter(s): Edmond Deuser; Jeff T. Heaton, Ph.D.

Most predictive models are intended to be used by more people than the data scientists who produced them, but integrating a predictive model created in R or Python into a larger system presents a challenge. An application programming interface (API) allows models to be easily accessed by other systems, both within and outside of an organization. When using an API, it is critical to consider aspects such as security, accounting, support, service level agreements (SLA) and the access patterns of clients. This presentation will explore these considerations and discuss technical details involved, such as cloud deployment, high availably and data exchange formats.

Track: Advanced Practitioner

Session Coordinator(s)

Facilitator(s)

Presentation(s): View Presentation

Competency: Results-Oriented Solutions

Moderator(s): Jeevan Duggempudi

Presenter(s): Gregory P. Heck, Vincent J. Granieri, FSA, MAAA, EA

Predictive models have disrupted the traditional underwriting process. In this session, we will review a decade in the life underwriting of a disruptor – the confluence of actuaries, underwriters and clinicians. In addition, perspectives of other stakeholders will be explored.

Track: Manager/Supervisor

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Michael David Hoyer, FSA, MAAA

Presenter(s): Michael Cletus Niemerg, FSA, MAAA

This session will walk the user through a simple case study to evaluate the practical aspects of building a regression model. Topics that will be covered include: stepwise selection, penalized regression and extensions such as generalized linear models and general additive models.

Track: Beginner/Implementer 

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Professional Values

Moderator(s): Stuart Klugman, FSA, CERA, Ph.D.

Presenter(s): Rohan Noel Alahakone, ASA, MAAA; Dorothy L. Andrews, ASA, MAAA

Actuarial tools now include both open and closed source software; both need a strong model governance framework to mitigate potential sources of model risk and external threats. This presentation will discuss how to develop, install and monitor a model governance framework to safeguard company modeling assets and intellectual capital.

Track: Advanced Practitioner

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Brian Chen

Presenter(s): Randal Olson

This session will cover what automated machine learning (AutoML) is, why is it important and how can it be integrated into prediction workflows.

Track: Manager/Supervisor 

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Technical Skills & Analytical Problem Solving

Moderator(s): Marshall Lagani

Presenter(s): Ben Johnson

This session will demonstrate how sufficiency in the R programming language also allows the R programmer to build a web application using R shiny. This session will focus how to present the results of predictive analytics (PA) in an easy-to-use and interactive way.

Track: All

Session Coordinator(s) Anders Larson, FSA, MAAA

Facilitator(s)

Presentation(s): View Presentation

Competency: Leadership

Moderator(s): Bradley Joseph Lipic

Presenter(s): Jordan Durlester

Now that many organizations have successfully proved the concept of data science as a value generator, the demand curve has hockey-sticked. Scaling data science linearly is not a practical approach, and it must be decided which projects should be worked on immediately, be delayed and be shelved. In this session, the process of developing a data science prioritization framework will be covered; from getting buy-in to implementation best practices.

Track: All

Session Coordinator(s) Rosmery Cruz

Facilitator(s)