Predictive Modeling and Big Data



Similar documents
Predictive Modeling Techniques in Insurance

Predictive modelling around the world

Session 62 TS, Predictive Modeling for Actuaries: Predictive Modeling Techniques in Insurance Moderator: Yonasan Schwartz, FSA, MAAA

Health Spring Meeting May 2008 Session # 42: Dental Insurance What's New, What's Important

An Introduction to Data Mining

COPYRIGHTED MATERIAL. Contents. List of Figures. Acknowledgments

Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions

Principles of Data Mining by Hand&Mannila&Smyth

SOA 2013 Life & Annuity Symposium May 6-7, Session 30 PD, Predictive Modeling Applications for Life and Annuity Pricing and Underwriting

An Introduction to Advanced Analytics and Data Mining

Learning outcomes. Knowledge and understanding. Competence and skills

BIOINF 585 Fall 2015 Machine Learning for Systems Biology & Clinical Informatics

Data Mining Applications in Higher Education

A Deeper Look Inside Generalized Linear Models

Predictive Modeling in Workers Compensation 2008 CAS Ratemaking Seminar

Session 35 PD, Predictive Modeling for Actuaries: Integrating Predictive Analytics in Assumption Setting Moderator: David Wang, FSA, FIA, MAAA

DATA MINING TECHNIQUES AND APPLICATIONS

A Basic Guide to Modeling Techniques for All Direct Marketing Challenges

Model Validation Techniques

Applied Data Mining Analysis: A Step-by-Step Introduction Using Real-World Data Sets

PREDICTIVE MODELS IN LIFE INSURANCE

An Overview of Knowledge Discovery Database and Data mining Techniques

Comparison of Data Mining Techniques used for Financial Data Analysis

Knowledge Discovery and Data Mining. Bootstrap review. Bagging Important Concepts. Notes. Lecture 19 - Bagging. Tom Kelsey. Notes

Welcome. Data Mining: Updates in Technologies. Xindong Wu. Colorado School of Mines Golden, Colorado 80401, USA

Practical applications of Predictive Modelling Overview of the process, the techniques and the applications

New Work Item for ISO Predictive Analytics (Initial Notes and Thoughts) Introduction

Using Predictive Analytics to Detect Fraudulent Claims

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

In this presentation, you will be introduced to data mining and the relationship with meaningful use.

Data Mining Part 5. Prediction

MS1b Statistical Data Mining

ElegantJ BI. White Paper. The Competitive Advantage of Business Intelligence (BI) Forecasting and Predictive Analysis

WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat

Data Mining. Nonlinear Classification

Azure Machine Learning, SQL Data Mining and R

Predictive Analytics Techniques: What to Use For Your Big Data. March 26, 2014 Fern Halper, PhD

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

HT2015: SC4 Statistical Data Mining and Machine Learning

Using Data Mining for Mobile Communication Clustering and Characterization

Personal Auto Predictive Modeling Update: What s Next? Roosevelt Mosley, FCAS, MAAA CAS Predictive Modeling Seminar October 6 7, 2008 San Diego, CA

from Larson Text By Susan Miertschin

The Predictive Data Mining Revolution in Scorecards:

SEIZE THE DATA SEIZE THE DATA. 2015

Predictive Analytics: Achieving Greater Decision Accuracy, Better Risk Segmentation, and Greater Profitability

How To Understand Data Mining In R And Rattle

Insurance Analytics - analýza dat a prediktivní modelování v pojišťovnictví. Pavel Kříž. Seminář z aktuárských věd MFF 4.

How To Make A Credit Risk Model For A Bank Account

TNS EX A MINE BehaviourForecast Predictive Analytics for CRM. TNS Infratest Applied Marketing Science

Session 54 PD, Credibility and Pooling for Group Life and Disability Insurance Moderator: Paul Luis Correia, FSA, CERA, MAAA

Data Mining. Dr. Saed Sayad. University of Toronto

Big Data and Marketing

Machine Learning and Data Mining. Fundamentals, robotics, recognition

Cleaned Data. Recommendations

Session 61 L, Applications of Data Analytics in Health Insurance. Moderator/Presenter: Henning Chiv, FSA, MAAA

MSCA Introduction to Statistical Concepts

Let the data speak to you. Look Who s Peeking at Your Paycheck. Big Data. What is Big Data? The Artemis project: Saving preemies using Big Data

Using Adaptive Random Trees (ART) for optimal scorecard segmentation

Insurance Fraud Detection: MARS versus Neural Networks?

Ensemble Learning Better Predictions Through Diversity. Todd Holloway ETech 2008

COMP 598 Applied Machine Learning Lecture 21: Parallelization methods for large-scale machine learning! Big Data by the numbers

Session 60 PD, Predictive Modeling Real Applications in Life Insurance and Annuities. Moderator: Ricardo Trachtman, FSA, MAAA

Data Mining: STATISTICA

Benchmarking of different classes of models used for credit scoring

Combining Linear and Non-Linear Modeling Techniques: EMB America. Getting the Best of Two Worlds

Handling attrition and non-response in longitudinal data

Machine learning for algo trading

2011 Data Miner Survey Highlights

Predictive Data modeling for health care: Comparative performance study of different prediction models

Promises and Pitfalls of Big-Data-Predictive Analytics: Best Practices and Trends

Data Mining + Business Intelligence. Integration, Design and Implementation

A Property & Casualty Insurance Predictive Modeling Process in SAS

High-Performance Analytics

Data Science with R. Introducing Data Mining with Rattle and R.

Application of SAS! Enterprise Miner in Credit Risk Analytics. Presented by Minakshi Srivastava, VP, Bank of America

KnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES

Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics

MSCA Introduction to Statistical Concepts

Chapter 12 Discovering New Knowledge Data Mining

Data Mining Algorithms Part 1. Dejan Sarka

Prediction of Stock Performance Using Analytical Techniques

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R

Data Mining with SAS. Mathias Lanner Copyright 2010 SAS Institute Inc. All rights reserved.

Session 42 PD, Predictive Analytics for Actuaries: Building an Effective Predictive Analytics Team. Moderator: Courtney Nashan

Knowledge Discovery and Data Mining

Studying Auto Insurance Data

Marketing Strategies for Retail Customers Based on Predictive Behavior Models

Location matters. 3 techniques to incorporate geo-spatial effects in one's predictive model

Data Mining Practical Machine Learning Tools and Techniques

Big Data and Data Science: Behind the Buzz Words

What is Data Mining, and How is it Useful for Power Plant Optimization? (and How is it Different from DOE, CFD, Statistical Modeling)

Transcription:

Predictive Modeling and Presented by Eileen Burns, FSA, MAAA Milliman Agenda Current uses of predictive modeling in the life insurance industry Potential applications of 2 1 June 16, 2014 [Enter presentation title in footer] Copyright 2007

Agenda Current uses of predictive modeling in the life insurance industry Potential applications of 3 Predictive Modeling 2010 SOA call for papers on predictive modeling for life insurers: involves "mining" datasets and performing statistical analysis that may uncover unexpected relationships about the underlying risks that may indicate the likelihood of future outcomes for an insurer. 4 2 June 16, 2014 [Enter presentation title in footer] Copyright 2007

Types of Predictive Models Ordinary Least Squares Generalized Linear Models Generalized Additive Models Bayesian Models CART Boosted trees Random forests K-means clustering Linear programming 5 Nonlinear programming Simulated annealing Genetic algorithms Agent-based models Neural networks MARS Robust regression Semiparametric regression Ordinary Least Squares and Generalized Linear Models response 㐲 0response 1 p predictor predictor predictor 6 3 June 16, 2014 [Enter presentation title in footer] Copyright 2007

CART and Random Forests 7 K-means Clustering 8 4 June 16, 2014 [Enter presentation title in footer] Copyright 2007

Optimization models, and Simulated Annealing 9 Agent-based Models 10 5 June 16, 2014 [Enter presentation title in footer] Copyright 2007

Agenda Current uses of predictive modeling in the life insurance industry 101010010111010010100010001010100101010010101 Potential applications 101010011010100100101010101010100100010101001 of 010110101000000100110101001000101010010101101 010000001011010101010101000101010010101101010 000001001010101010001010100101011010100000010 010101010100010101001010110101000000100101010 101000101010010101101010000001001010101010001 010100101011010110101001001010101010011010101 010000001001010101010001010100101011010100000 010010101010100010101001010110101000011010100 11 Dimensions of data 12 Volume Velocity Variety Implications for modeling with Imposes restrictions on data cleaning feasibility Requires new methods for analysis Shifts focus to correlation rather than causation Implications for users of Why? Precision 1010100101110100101000100010101 0010101001010110101001101010010 0101010101010100100010101001010 1101010000001001101010010001010 1001010110101000000101101010101 0101000101010010101101010000001 0010101010100010101001010110101 6 June 16, 2014 [Enter presentation title in footer] Copyright 2007

Types of Personal 13 Credit Consumer spending Health Social network Fit bit Grouped Regional Employer Healthcare provider Demographic 1010100101110100101000100010101 0010101001010110101001101010010 0101010101010100100010101001010 1101010000001001101010010001010 1001010110101000000101101010101 0101000101010010101101010000001 0010101010100010101001010110101 Some restrictions apply! Agenda Current uses of predictive modeling in the life insurance industry Potential applications of q x 14 7 June 16, 2014 [Enter presentation title in footer] Copyright 2007

Current uses of predictive modeling in the life insurance industry Underwriting Pricing Policyholder behavior q x Agent performance Product design 15 Agenda Current uses of predictive modeling in the life insurance industry Potential applications of 0123 4567 8901 2345 06/14 John Smith 16 8 June 16, 2014 [Enter presentation title in footer] Copyright 2007

Property & Casualty Insurance Car insurance Homeowners Workers comp Health Credit Bureaus 0123 4567 8901 2345 06/14 John Smith 17 Agenda Current uses of predictive modeling in the life insurance industry 101010010111010010100010001010100101010010101 Potential applications of 101010011010100100101010101010100100010101001 010110101000000100110101001000101010010101101 010000001011010101010101000101010010101101010 000001001010101010001010100101011010100000010 010101010100010101001010110101000000100101010 101000101010010101101010000001001010101010001 010100101011010110101001001010101010011010101 010000001001010101010001010100101011010100000 010010101010100010101001010110101000011010100 q x 18 9 June 16, 2014 [Enter presentation title in footer] Copyright 2007

Potential Applications of Directional marketing Scoring algorithms for underwriting Further refine experience analysis Base and dynamic lapse Guarantee utilization Annuitization 1010100101110100101000100010101 0010101001010110101001101010010 0101010101010100100010101001010 1101010000001001101010010001010 1001010110101000000101101010101 0101000101010010101101010000001 0010101010100010101001010110101 q x 19 Q&A Eileen Burns, FSA, MAAA eileen.burns@milliman.com 206-504-5955 1010100101110100101000100010101 0010101001010110101001101010010 0101010101010100100010101001010 1101010000001001101010010001010 1001010110101000000101101010101 0101000101010010101101010000001 0010101010100010101001010110101 0123 4567 8901 2345 06/14 John Smith q x 20 10 June 16, 2014 [Enter presentation title in footer] Copyright 2007

2026 © DocPlayer.net Privacy Policy | Terms of Service | Feedback  | Do Not Sell My Personal Information