Predictive Analytics 4.0 Virtual Event

Thursday, September 24

9:00 – 10:00 AM CT General Session – Human Values in the Loop: Principles for Ethical AI Moderator: Xiaojie Wang, FSA, CERA Presenter: James Guszcza, 2020-21 Fellow, Center for Advanced Study in the Behavioral Science Session Description: AI might be the new electricity, but as algorithms impact more aspects of our lives, there is a growing concern that AI is more than a computer science problem—it is also a societal problem. As statistically savvy professionals with training in law, regulation, and professionalism, actuaries can play an important role in helping ensure that AI technologies properly reflect important societal values. This discussion will review the core principles of bioethics and discuss how they apply to the fields of data science and AI.

10:00 – 10:30 AM CT Break

10:30 – 11:30 AM CT Morality in the Machine: Ethics and the Rise of AI in the Insurance Industry Credits: 1.20 SOA CPD Compentency: Results-Oriented Solutions Moderator: Toby L. Hall, FSA, MAAA Presenter(s): Jonathan Culbert Presentation Session Description: Artificial intelligence built on machine learning is being rapidly adopted throughout the insurance industry. From fraud detection to product design to claims processing, AI is transforming the way insurance companies do business. While the benefits of AI are significant, its acceptance also comes with risks and ethical questions. Unintentional algorithmic bias and lack of transparency can lead to unfair outcomes and reputational damage to companies. What pitfalls should practitioners of machine learning and AI seek to avoid, and what tools are available to ensure that AI is implemented as fairly and ethically as possible? Experience Level: Beginner/Implementer Model Validation for Managers Credits: 1.20 SOA CPD Compentency: Results-Oriented Solutions Moderator(s): Gershon Henoch Firestone, FSA, MAAA Presenter(s): Kilian Blum; Boyi Xie Session Description: At some point in any predictive analytics project, managers must decide whether model performance is sufficient for deployment. Mistakes can lead to significant adverse consequences when machine learning based costing, underwriting or persistency models do not perform as expected. Furthermore, under certain circumstances, metrics like accuracy can be deeply misleading when assessing model performance. This session will not only investigate the different metrics but also look at case studies to illustrate how metrics behave under unique circumstances. Most importantly, these case studies will show how the underlying business objective determines which metric should be focused on. Experience Level: Manager/Supervisor

11:30 AM – 12:00 PM CT Break

12:00 – 1:00 PM CT Applying Predictive Analytics in Underwriting Requirement Determinations Credits: 1.20 SOA CPD Compentency: Technical Skills & Analytical Problem Solving Moderator(s): Rosmery Cruz Presenter(s): Guizhou Hu Presentation Session Description: Age and face amount conventionally drive underwriting decisions, but what happens when the underwriter encounters two applicants of differing medical profiles? Should the underwriter be required to pull a complete set of medical information for both? In this presentation, we will introduce a more dynamic approach to assessing underwriting requirements by integrating predictive models with actuarial protective value assessments. This new approach streamlines accelerated underwriting programs, saving time and money for the carrier and applicant alike. Find out how insurers can transition from conventional AA requirement concepts into a more individualized underwriting paradigm. Experience Level: All Quantifying Prediction Uncertainties Credits: 1.20 SOA CPD Compentency: Technical Skills & Analytical Problem Solving Moderator: Kimberly M. Steiner, FSA, MAAA Presenter: Dan Kim, FSA, CERA, MAAA Session Description: Predictions will likely be inaccurate as a model is a generalized version of the reality. In this session, the presenter will discuss a framework to understand and quantify prediction uncertainties. They will discuss statistical methods such as Frequentist and Bayesian approaches to quantify model uncertainties. Lastly, the presenter will share some practical considerations such as experimental designs, interpretation of models and outputs to inform the level of prediction uncertainty. Experience Level: Manager/Supervisor

1:00 – 1:30 PM CT Break

1:30 – 2:30 PM CT Is There Anything More Practical Than a GBM? Credits: 1.20 SOA CPD Compentency: Technical Skills & Analytical Problem Solving Presenter(s): Shea Parkes, FSA, MAAA; Erica Rode, ASA, MAAA, Ph.D. Session Description: Gradient Boosting Machines (GBM) are effective with many problems. They strike a great balance between accuracy and interpretability. This session will be an ode to the GBM. It will include a bit of history about who came up with the original idea, who helped refine the idea into statistical learning, and who helped write the amazing modern-day implementations. We'll ask the attendees to share their own love stories or hate mail if they're brave enough. Experience Level: Beginner/Implementer Modeling Mortality Using Generalized Additive Models: A Case Study Credits: 1.20 SOA CPD Competency: Technical Skills & Analytical Problem Solving Presenters: John Myslinski, ASA Presentation Session Description: There are several statistical and machine learning techniques for modeling mortality. In this session, we will discuss the various approaches and demonstrate how you can use the techniques from Generalized Additive Models (GAM) to model mortality. The session will be structured as a case study of a pricing actuary setting the mortality assumption for a product. The presenter will walk through the framework for building a GAM in R with the mgcv-package, highlighting major modelling decision points along the way. We will then wrap up the session with an overview of ways to validate your resulting mortality assumption. Experience Level: Advanced Practitioner Networking Session 1A: Accelerated Underwriting Moderator(s): David L. Snell, ASA, MAAA ; Xiaojie Wang, FSA, CERA Session Description: Accelerated Underwriting remains the most common application for predictive analytics within insurance companies. This moderator led discussion will inform you of what tools and methodologies have been successful, common challenges and pitfalls to creating and maintaining an accelerated underwriting program. Regulatory issues will also be discussed as the industry weighs the benefits of making insurance more accessible against having less transparency from model-based decisions. Networking Session 1B: Ethical Use of Data and Identifying Bias in Models Moderator(s): David Moore, FSA, MAAA Session Description: Insurance uses several data points on an applicant to assess their risk, and pools them with other similar risks to manage the risk and provide a fair price to the insured. The advances in data collection and advanced analytics enable us to understand risk profiles better than ever before, but as we automate the pricing and underwriting process, are we using big data ethically to do this, and are we inadvertently using biased models to make underwriting decisions? This session will be an open discussion around what we as actuaries can and should do when using predictive models in an ethical manner.

2:30 – 3:00 PM CT Break

3:00-4:00 PM CT Automated Machine Learning and Understanding its Potential Credits: 1.20 SOA CPD Compentency: Results-Oriented Solutions Moderator: Rosmery Cruz Presenter(s): Jeff T. Heaton, Ph.D. Presentation Session Description: Machine learning experts are attempting to use automation to solve labor shortages across many industries. Within the insurance industry, tasks such as underwriting, fraud detection, forecasting, and marketing could benefit from increased efficiency through both machine learning automation and augmentation. Machine learning experts also apply this same automation to more routine job tasks through a process known as Automated Machine Learning (AutoML). With AutoML you can augment a data science team or capture data even if you do not have access to data scientists. This trend has many positive benefits including data democratization and the rise of citizen data scientists. Moreover, this presentation will provide an overview of both commercial and open-source AutoML and the platforms strengths and weaknesses. Experience Level: Manager/Supervisor Predictive Models in Stop-Loss Insurance Credits: 1.20 SOA CPD Compentency: Technical Skills and Analytical Problem-Solving Presenter(s): Michael Cletus Niemerg, FSA, MAAA Session Description: In this session, explore the application of predictive models in pricing stop-loss coverage. The presenter will first explain the differences between stop-loss coverage and fully-insured coverage, why predictive models have the potential to increase pricing accuracy, and why the nature of stop-loss coverage makes prediction particularly difficult. Followed by considerations and challenges in adapting fully insured cost models for use in stop-loss, as well as techniques and considerations when building models customized for stop-loss. Lastly, we will cover general pricing considerations, hurdles to adaptation, and practical considerations in the development and use of predictive models. Experience Level: Advanced Practitioner

Friday, September 25

Presenters and Moderators

James Guszcza, 2020-21 Fellow, Center for Advanced Study in the Behavioral Science

Jim Guszcza is a Fellow of the Casualty Actuarial Society and Society of Actuaries.  He was a member of Deloitte’s original data science practice and was the first to be designated Deloitte’s US Chief Data Scientist. In the 2020-21 academic year, Jim will be a fellow at the Center for Advanced Study in the Behavioral Sciences at Stanford University.

Lindsay Allen, ASA

As a data scientist and credentialed actuary, Lindsay works with business and data teams to enhance healthcare product development and pricing strategies with predictive analytics.

She has experience optimizing rating systems for Medicare Advantage and Commercial health plans and managing advanced analytics solutions to improve risk adjustment, medical economics, and care management processes.

Lindsay has led teams in developing geospatial analyses, Excel tools, Bayesian statistic simulations and predictive models using SQL, VBA, Python, and R. These projects have helped to maximize joint venture performance, better assess value at risk, and quantify the impact of plan design on members choice of providers.

Lindsay is currently a lead data scientist at Aetna, a CVS Health Company.

Kilian Blum

Kilian is the Head of Swiss Re's Digital and Smart Analytics Team (DSA) in the Americas. DSA works across all lines of business to foster automation and advanced insights generation as well as to develop data driven business models for Swiss Re and its clients.

Kilian joined Swiss Re and DSA as an analytics consultant before accepting the role of tech transformation and strategy manager. Over the past years, Kilian has successfully led various DSA projects including many client engagements, developing and running global innovation bootcamps and driving key strategic initiatives around data and technology with senior management.

Kilian studied in St. Gallen (HSG, Switzerland) and Medford (Tufts, US) as part of a double degree program focused on International Business Relations. Before joining Swiss Re Kilian worked at the St. Gallen Symposium and for an international management consulting company.

Patrick Jason Colbert, FSA, MAAA

Patrick Colbert has 13 years of actuarial experience that includes working with various health plans and provider organizations. Presently, Pat leads actuarial services for New Century Health, Evolent Health's specialty care management subsidiary. Pat joined Evolent in July 2015 and has supported a wide variety of customers including Medicaid MCOs in Florida and Texas as well as a variety of ACOs participating in MSSP, NGACO, and private payer partnerships. Before joining Evolent, Pat worked as an Analytic Manager focused on clinical program analytics at Tufts Health Plan and previously worked in the retirement line of business at Towers Watson.

Rosmery Cruz

In her role as Senior Data Scientist for RGA, Rosmery Cruz leads the scoping, building, and implementation of predictive models for internal and external clients. She also developed and now leads an enterprise-wide program to educate RGA associates about predictive analytics and RGA’s client offerings in this area. In addition, Rosmery heads RGA’s predictive analytics community of practice, uniting those involved in predictive analytics throughout the organization to establish best practices, centralize collaboration, and share expertise. Prior to joining RGA in 2017, Rosmery was Client Analyst with ForeSee, where she conducted research for the company’s Fortune 500 clients to enable evidence-based solutions to critical business objectives. Before that, Rosmery served as Senior Analyst, Marketing Sciences for MaritzCX, performing statistical research on customer satisfaction as well as client acquisition and retention on behalf of Fortune 500 companies. She also developed surveys that tracked key business metrics. Rosmery received her Bachelor of Science in political science and government and Master of Science in political science from Florida State University.

Jonathan Culbert

Jonathan Culbert is Principal Data Scientist at Delta Dental of Michigan, Ohio, and Indiana, where he has worked since 2010. He attended the University of Michigan College of Engineering but left school to pursue a career in broadcasting. Jonathan worked as a radio news anchor, reporter, and morning show host in Detroit before returning to school to earn a B.S. in Economics from Eastern Michigan University. In addition to his work at Delta Dental, Jonathan studies at Carnegie Mellon University, where he expects to graduate with a Master of Science in Business Analytics in May 2021. Jonathan's primary areas of study at Carnegie Mellon include machine learning, optimization, and data-driven leadership, and he is especially interested in issues of fairness and accountability in machine learning.

Jordan Durlester

Jordan Durlester is executive director of data strategy at Reinsurance Group of America. He builds and scales advanced analytics organizations and implements actionable data strategies designed for specific markets. His role requires understanding the data and analytics landscape and designing next-generation data solutions to meet current and future needs. He has been with RGA since 2017.

Prior to joining RGA, Durlester was a data analyst and business intelligence solutions adviser for Centene, assigned to special projects, including scaling business intelligence (BI) practices across 26 nationwide offices and ensuring standards were met.

Durlester received a masters of public health (MPH) from Washington University in St. Louis and has a bachelor of arts in political science from the University of Arizona.

Maria Govorun, PhD

Maria Le Calvez is a Director, Integrated Analytics at Munich Re. She is enjoying combining her experience in academia and practice to build predictive models to optimize processes in Life and Disability insurance.

In 2009, Maria completed her PhD in applied math being primarily focused on phase-type modeling of ageing and mortality. Prior to joining Munich Re in 2015, she participated in a number of research projects funded by organizations such as the French Community in Belgium and Fields Institute in Canada.

Prior to her academic career, Maria was a life actuary in AIG Life Russia in parallel working for an actuarial consulting start-up dealing with valuation of employee benefits and stochastic modeling for pension funds.

Martha Grace

I completed undergraduate study at Smith College (Psychology, Applied Statistics minor) and graduate work at University of Massachusetts, Amherst (MS Computer Science). As a data scientist, I work on algorithmic life underwriting models for MassMutual Life Insurance Co. My research interests include algorithmic fairness and applying machine learning to social problems.

Toby L. Hall, FSA, MAAA

Toby Hall, senior vice president, chief actuary and chief data officer, joined Delta Dental of Michigan, Ohio, and Indiana in 2003. Hall operates as appointed actuary, performing monthly reserve calculations, surplus modeling, product design and pricing, yearly compliance filings, and manages the five-year corporate forecast. Prior to his current role, Hall was vice president and chief actuary and associate actuary.

Hall serves on the board of directors for Dansk Tandforsikring Administration and Global Dental Insurance A/S and oversees underwriting and data governance in Michigan, Ohio and Indiana. Hall is also a member of the American Academy of Actuaries, a fellow of the Conference of Consulting Actuaries and a fellow of the Society of Actuaries. Hall earned his bachelor’s degree from Western Michigan University and his master’s degree from the University of Michigan.

Matthew Kevin Heaphy, FSA, MAAA

Matt Heaphy is a Vice President and Actuary at Nassau. He leads the Data Analytics team which provides a broad range of services including data warehouse projects, enterprise-wide reporting, experience studies, and cross-functional analytics support. Previously, he held a variety of roles at Nassau leading life and annuity pricing, and fixed indexed annuity product management, valuation, and hedging functions. Matt is passionate about programming, data visualization, predictive modeling, and all things R. Matt holds a Bachelor of Science Degree from the University of Connecticut, is a Fellow of the Society of Actuaries, and is a Member of the American Academy of Actuaries.

Jeff T. Heaton, Ph.D.

Jeff is a data scientist with a strong computer science background, including over 20 years of experience of all aspects of software development. At RGA Jeff is senior data scientist developing underwriting models and has worked with reinsurance treaties, retrocession, and research. As a prolific writer, speaker, and programmer he is very active in the open source community. As the creator of the Encog machine learning framework he has coded his own implementations of algorithms such as deep learning neural networks, GLMs, genetic programming, and other algorithms. Python, R, Java/C#, Javascript, and C/C++ are his primary programming languages. Jeff holds numerous certifications and credentials, such as the Johns Hopkins Data Science certification, Fellow of the Life Management Institute (FLMI), ACM Upsilon Pi Epsilon (UPE), a senior member of IEEE, and a top 10% placement in a Kaggle competition. He has published his research through peer review ed papers with the Journal of Machine Learning Research and IEEE. Jeff has a master’s degree in Information Management from Washington University, and is a Ph.D. candidate in computer science at Nova Southeastern University. His dissertation topic is feature engineering for machine learning.

Guizhou Hu

Mr. Guizhou Hu received medical degree from Beijing Medical University, and PhD in epidemiology from Cornell University. His background and expertise have been centered on epidemiological research and predictive analytics in healthcare and insurance medicine.

Before Joining RGA in April 2018, He served as Chief Decision Analytic at Gen Re for 3 years. He was responsible on data and analytic strategy, machine learning technique and prediction model development for underwriting. Before that he served as Chief Scientific Officer at BioSignia Inc. for 17 years. In addition to leading their mortality underwriting research and overseeing peer review publications, He was responsible for creating new statistical techniques and developing predictive models intended for healthcare and life insurance underwriting.

Dr. Guizhou has joined RGA in the role of VP, Head of Risk Analytics. He brings to RGA a combination of medical training and long experience in underwriting related analytics

Clinton Innes, FSA, FCIA

Clinton Innes is a Sr. Data Scientist at Munich Re where he works using data to help solve insurance claims and underwriting challenges. He holds a Masters in Applied and Computational Mathematics from Simon Fraser University and is a Fellow of the Society of Actuaries.

Dan Kim, FSA, CERA, MAAA

Dan is a director with the Insurance Consulting Technology business of Willis Towers Watson in Atlanta. He has been with Willis Towers Watson since 2007. His experience includes financial reporting and risk management with expertise in IFRS 17, embedded value, economic scenario generation, economic capital modeling, and predictive analytics.

Dan is a key member of the firm’s IFRS 17 team monitoring the impacts throughout the industry, assessing the impact on our clients and developing strategies and solutions to help clients successfully prepare and implement IFRS 17. Dan’s IFRS 17 related experience includes financial reporting framework development, IFRS 17 calculation engine implementation, developing and reviewing guidance notes/technical papers, and financial impact analysis using IFRS 17 calculation engine.

Dan has used predictive modeling tools for experience study, model efficiency, risk analysis purposes. He supports Predictive Analytics Initiative at Willis Towers Watson in researching and published an article on using Bayesian analysis to review uncertainty of predictions.

Dihui Lai, ASA

Dihui Lai is a Lead Data Scientist in RGA. In this role, Dihui uses machine learning and predictive modeling for various insurance applications, including artificial intelligence (AI)-augmented underwriting, predictive model-based pricing, and lapse experience studies. He has experience in quantitative modeling, nature language processing and document image processing. Before joining RGA in 2012, Dihui was a research associate at the University of Maryland. His research and academic background include computational neuroscience, information processing in visual neural networks, and high-energy physics. Prior to that, Dihui worked on neuromorphic system design at Qualcomm. Dihui also has multiple peer reviewed articles published in neuroscience, underwriting journals and SOA newsletter.

Bradley Joseph Lipic

As Vice President, Head of Global Data Strategy for RGA, Brad Lipic provides strategic leadership and oversight for the Enterprise’s data acquisition and analytics strategy, with the imperative to enable the organization to make better plans and decisions with data and analytics. Brad joined RGA in 2015 and now has 15 years of experience in designing, developing and deploying predictive modeling and analytic solutions in multiple industries. Prior to joining RGA, Brad worked as a Management Consultant for Deloitte Consulting, LLP advising multiple Fortune 500 companies on how to grow their business analytics capabilities through effective use of internal and external data, technology, processes and operating models. Brad’s analytics experience spans financial services, insurance, life sciences, healthcare, consumer and industrial products, and federal and state governments. Brad possesses an actuarial background, and received a B.S. degree in actuarial science, with computer science and business administration focus, from Maryville University of St. Louis.

John Myslinski, ASA

John Myslinski is an Associate of the Society of Actuaries and a Data Scientist on the Integrated Analytics team at Munich Re Life US, where he uses analytic techniques and new data sources for assumption development, pricing and risk assessment. While he currently works as a data scientist, the bulk of his experience has been in traditional actuarial roles. Previously he worked in Individual Life Reinsurance Pricing at Munich Re US Life and in various pricing roles at Guardian Life Insurance.

Michael Cletus Niemerg, FSA, MAAA

Michael Niemerg, FSA MAAA, is Predictive Modeling Manager at Milliman IntelliScript.

Shea Parkes, FSA, MAAA

Shea Parkes is a principal at Milliman, Inc. where he manages a healthcare practice focused on product development. Many of their solutions apply predictive analytics to population health opportunities. Shea has personal expertise in machine learning, applied statistics, data engineering, product development and software engineering. In addition to this data science emphasis, he also maintains his actuarial roots on risk scoring, forecasting and reserving.

Kevin J. Pledge, FSA, FIA

Serial entrepreneur, specializing in online distribution solutions for insurance organizations. Previous to entrepreneurial move, held positions with senior strategic and operational responsibility for insurance organizations in the UK and Canada. Responsible for not only the actuarial function, but marketing, compliance and administrative areas as well. Left this to start an analytics firm catering to the insurance industry, led this firm for 15 years. Kevin is qualified as a Fellow of the Institute of Actuaries (UK) and a Fellow of the Society of Actuaries.

Neil Raden

Neil Raden is an industry analyst and advisor, and founder of Hired Brains Research. He's been an actuary, statistician, software engineer and consultant for three decades. His specialties are the use of analytics to drive decision-making. He is the co-author of the book ''Smart (enough) Systems, Prentice-Hall, 2007 and dozens of journal articles, whitepapers, presentations, and keynotes in the US and abroad. He has been a guest presenter many times at SOA events. Neil is the Principal Investigator and author of the 2019 report ''Innovation and Technology: Ethical Use of Artificial Intelligence for Actuaries.”

Robert Jason Reed, FSA, MAAA

Jason Reed is a health actuary and statistician at Optum, specializing in predictive modeling and statistical applications to health insurance. He has over 10 years of experience applying advanced analytics to claims forecasting, risk scoring, and health risk stratification. He is a Fellow of the Society of Actuaries and has Master’s Degrees in Statistics and Applied Mathematics from Texas A&M University and currently lives in Boston.

Erica Rode, ASA, MAAA, Ph.D.

Erica is an actuary with the Minneapolis office of Milliman. She joined the firm in 2013.Erica works in health insurance, with a focus on risk adjustment and predictive modeling of healthcare costs. Erica has extensive experience designing and calibrating predictive models, and she is involved in ongoing research and development of the models included in the Milliman Advanced Risk Adjusters (MARA) software. She has also developed custom predictive models for application in unique circumstances, and has worked with models in the context of commercial, Medicare, and Medicaid populations.

Mark Andrew Sayre, FSA, CERA

Mark Sayre focuses on Data Ethics, Governance and Privacy at Haven Life.  Previously, Mark Sayre led Risk Solutions at Haven Life, where he oversaw Actuarial, Analytics, Reinsurance and Underwriting Technology teams for MassMutual's direct-to-consumer and digital transformation initiatives. In his more than 5 years working at Haven Life, Mark has led the development of numerous innovations, including automated and accelerated underwriting, AI-powered actuarial models and novel product designs.  Mark received a Bachelor's of Arts in Economic Theory from New York University and a Master's of Science in Economics and Management from Universita' Commerciale Luigi Bocconi. He became a Fellow in the Society of Actuaries in March 2016.

Kimberly M. Steiner, FSA, MAAA

Kim Steiner, FSA, MAAA, is a Senior Director and leads the analytics and experience analysis initiatives of the Willis Towers Watson America’s life practice.  Kim is one of the firm’s experts in several areas, including predictive modeling, assumption setting, term insurance, principle-based reserving, mortality and COLI/BOLI products. Prior to joining Willis Towers Watson in 2008, Steiner worked as an actuary at Great-West Life and Annuity. She is a fellow of the Society of Actuaries and a member of the American Academy of Actuaries. She has written several articles and is a frequent speaker at industry meetings.

Boyi Xie

Boyi Xie is Head of Data Science Americas at Swiss Re. His work focuses on developing AI-backed insurance solutions for different lines of business, including Life & Health, and Property & Casualty insurance. His work has helped digital transformation and the adoption of AI/machine learning in insurance products and operations. Boyi obtained his Ph.D. from Columbia University. He published scientific articles and is a reviewer of top research venues in artificial intelligence.

Registration Fees

Entire Meeting

*To utilize the individual 5+ rate you must first have 4 full paying registrants already registered. Once you have reached the 5th registration you may begin to utilize this discount. To confirm eligibility for this discount please email Lisa Collins at lcollins@soa.org

• This discount cannot be combined or used with one- or two-day registration or any already discounted rates.

Registration Includes

• One access code to the live virtual seminar
• One Internet connection for streaming only
• One set of presentation materials
• Access to all available session recordings

Payment Information

• Register online, via credit card, no later than September 21. A confirmation of your registration will be sent by email.
• Full payment is required at the time of registration.
• For information regarding check payment please contact Customer Service. Please allow 10 days for receipt and processing of checks.
• Refunds will not be issued.

Table of Contents

Hack-A-Thon

Networking Sessions

Practical Predictive Analytics Seminar

Hack-A-Thon

For those looking for some truly hands-on experience, or those looking to demonstrate their machine learning expertise through a friendly competition, the Predictive Analytics and Futurism Section is proud to once again host the half-day Hack-A-Thon. Attendees who choose to participate in the Hack-A-Thon will compete on teams to build the most accurate model of a real dataset. The Hack-A-Thon is open to attendees of all skill levels, but this year we have teamed up with the PPAS to make this a great learning opportunity for beginners as well. This event will be fast, furious and fun, with four hours to develop a model and submit predictions.

Contest participants must be registered for Predictive Analytics 4.0.

Networking Sessions

This year we will be conducting a series of moderated networking sessions to enable a deeper dive on some key topics at Predictive Analytics 4.0. These sessions will be limited to 20 registrants per topic and take place opposite other traditional sessions, utilizing web meeting technology. The sessions will not be typical symposium presentations but will rely on having group participation to discuss the given topics. Please sign up for these sessions only if you are willing to actively participate and we encourage those participants to have their camera on. Additionally, by registering early you have a better chance to secure a spot for one of the networking sessions.

Practical Predictive Analytics Seminar

Introducing the new addition to Predictive Analytics 4.0, this experiential one-day seminar will teach attendees how to build a basic predictive model through generalized linear models using R. Participants will also learn how to communicate their modeling results to a less technical audience in a clear and understandable way.