2012 3 R’s and Predictive Modeling Boot Camp
Nov. 8- 9, 2012
Session #1: Predictive Modeling: An Overview
Predictive Modeling: An Overview
November 8, 2012 Syed M. Mehmud Wakely Consulting Group
Welcome!
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Day 1: Agenda
1.
Predictive Modeling, An Overview
2.
Software & Algorithms
3.
Exercises
4.
Risk Adjustment
5.
New Research in Risk Adjustment
Quick Check
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Which describes you?
1.
I am a healthcare actuary
Nov-12 3
Quick Check
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Which describes you?
1.
I am a healthcare actuary
Quick Check
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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
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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
What to take-away…
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Predictive modeling is mainstream now…
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For example, the practice of risk adjustment!
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A review of the 3Rs
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I can do it!
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Bona-fide Predictive Model
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Understand the 3R Program…a bit better
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A lingering sense of excitement, fun and possibility…
Nov-12 7
What to do…
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Do-it-yourself
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Ask questions
Predictive Modeling
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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
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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…?)
Predictive Modeling
“…is developing expectations about the future using statistical
methods.”
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Key ingredients
• Data, Methodology, Model
Nov-12 11
Predictive Modeling
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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
Predictive Modeling
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Work Principles
• Objectives, Strategy, and Tactics • Managing Expectation
• Managing Scope • Communication • Documentation
• Monitoring and Maintenance • Checklists!
• All about design!
Nov-12 13
The Algorithms
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Estimation
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Classification
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Clustering
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Simulation
The Algorithms
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Estimation
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Regression analysis
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Neural Networks
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Stochastic Machines
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Time-series methods
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Collaborative filtering
Nov-12 15The Algorithms
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Classification
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Discriminant analysis
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CART
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Rule based algorithms
The Algorithms
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Simulation
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Complexity approach
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Genetic algorithms
Nov-12 17The Algorithms
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Ensemble modeling
The Software
Nov-12 19
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Which others?
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 $$ Statistica Statistical algorithms, graphics $$
A Few Actuarial Applications
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Automobile insurance
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ACA and healthcare exchanges
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Risk adjustment
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Forecasting
A Few Actuarial Applications
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Wakely Procedure Forecasting model
Nov-12 21
Chart 1: All Payer Volume (Quarterly)
500 1,000 1,500 2,000 2,500 3,000 20 031 20 032 20 033 20 034 20 041 20 042 20 043 20 044 20 051 20 052 20 053 20 054 20 061 20 062 20 063 20 064 20 071 20 072 20 073 20 074 20 081 20 082 20 083 20 084 20 091 20 092 20 093 20 094 20 101 20 102 20 103 20 104 20 111 20 112 20 113 20 114 20 121 20 122 20 123 20 124 20 131 20 132 20 133 20 134 20 141 20 142 20 143 20 144 20 151 20 152 20 153 20 154 20 161 20 162 20 163 20 164 20 171 20 172 20 173 20 174 Q tr ly Di sc ha rge V ol ume
Volume Err1LB Err1UB Err2LB Err2UB
Historic Volume Forecast Volume
ACA Present Day
A Few Actuarial Applications
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Non-Traditional Variables in Risk
Adjustment
A Few Actuarial Applications
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Development of WRA
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National Risk Adjustment Simulation
Nov-12 23
Questions?
Nov-12
Syed M. Mehmud is a Director and Senior Consulting Actuary with Wakely Consulting Group, Inc. He can be reached at [email protected]
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