R s and Predictive Modeling Boot Camp Nov. 8-9, Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA

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1 R s and Predictive Modeling Boot Camp Nov. 8-9, 2012 Session #1: Predictive Modeling: An Overview Syed Muzayan Mehmud, ASA, FCA, MAAA

2 Predictive Modeling: An Overview November 8, 2012 Syed M. Mehmud Wakely Consulting Group Welcome! Day 1: Agenda 1. Predictive Modeling, An Overview 2. Software & Algorithms 3. Exercises 4. Risk Adjustment 5. New Research in Risk Adjustment 6. The Other 2Rs 7. Complexity Science 8. Information Visualization, Documentation & Communication Nov

3 Quick Check Which describes you? 1. I am a healthcare actuary Nov-12 3 Quick Check Which describes you? 1. I am a healthcare actuary 2. I build or review predictive models on a regular basis Nov

4 Quick Check Which describes you? 1. I am a healthcare actuary 2. I build or review predictive models on a regular basis 3. I have used a risk adjustment model Nov-12 5 Quick Check Which describes you? 1. I am a healthcare actuary 2. I build or review predictive models on a regular basis 3. I have used a risk adjustment model 4. My head is hurting from these power-point transition effects Nov

5 What to take-away Predictive modeling is mainstream now For example, the practice of risk adjustment! A review of the 3Rs I can do it! Bona-fide Predictive Model Understand the 3R Program a bit better A lingering sense of excitement, fun and possibility Nov-12 7 What to do Do-it-yourself Ask questions Share expertise! Nov

6 Predictive Modeling According to Marriam-Webster (ugh) It is: A process used in predictive analytics to create a statistical model of future behavior. Predictive analytics is the area of data mining concerned with forecasting probabilities and trends. A predictive model is made up of a number of predictors, variable factors that are likely to influence or predict future behavior. The end result is both a set of factors that predict, to a relatively high degree, the outcome of an event, as well as what that outcome will be. In marketing, for example, a customer s gender, age and purchase history might predict the likelihood of a future sale. To create a predictive model, data is collected for the relevant factors, a statistical model is formulated, predictions are made and the model is validated. The model may employ a simple linear equation or can be a complex neural network or genetic algorithm. Society of Actuaries Predictive Modeling Subcommittee, January 2012 Nov-12 9 Predictive Modeling Definitions are written by the definers It the process of creating a statistical model (is it a process?) Analytical methods to understand and predict customer behavior (is it related to a specific application?) It is a form of data-mining technology that works by analyzing historic and current data (is it a technology?) Predictive modeling is a technique used to predict future behavior and anticipate consequences of change (is it a technique?) It is the process of using software X in order to analyze patterns (is it software?) Nov

7 Predictive Modeling is developing expectations about the future using statistical methods. Key ingredients Data, Methodology, Model Nov Predictive Modeling Modeling Principles Counting, Mining, and Modeling u From data (to predictors) to decisions Notion of Predictability u Relation to model validation Uncertainty Occam s Razor Science vs. Art u Role of context and judgment Frequentist& Bayesian perspectives Sensitivity Testing Accuracy, Precision & Significance Nov

8 Predictive Modeling Work Principles Objectives, Strategy, and Tactics Managing Expectation Managing Scope Communication Documentation Monitoring and Maintenance Checklists! All about design! Nov The Algorithms Estimation Classification Clustering Simulation Nov

9 The Algorithms Estimation Regression analysis Neural Networks Stochastic Machines Time-series methods Collaborative filtering Nov The Algorithms Classification Discriminant analysis CART Rule based algorithms Lazy classifiers Nov

10 The Algorithms Simulation Complexity approach Genetic algorithms Nov The Algorithms Ensemble modeling Nov

11 The Software Software Description Cost SQL Mostly data management, macros & simulation $$ SAS Data management, statistical analysis and algorithms $$$ Rapid Miner Machine learning focus Free! R Statistical algorithms, graphics Free! Excel Handling lightweight data* $ Mathematica Symbolic manipulation and formulaic solving $$ Statistica Statistical algorithms, graphics $$ Which others? Nov A Few Actuarial Applications Automobile insurance ACA and healthcare exchanges Risk adjustment Forecasting Others? Nov

12 A Few Actuarial Applications Wakely Procedure Forecasting model Chart 1: All Payer Volume (Quarterly) 3,000 Historic Volume Forecast Volume 2,500 Qtrly Discharge Volume 2,000 1,500 1, Present Day ACA Volume Err1LB Err1UB Err2LB Err2UB Nov A Few Actuarial Applications Non-Traditional Variables in Risk Adjustment Nov

13 A Few Actuarial Applications Development of WRA National Risk Adjustment Simulation Nov Setting up an environment Nov

14 Questions? Syed M. Mehmud is a Director and Senior Consulting Actuary with Wakely Consulting Group, Inc. He can be reached at SyedM@Wakely.com PredictiveModeler.com Nov