Evaluating Inforce Blocks Of Disability Business With Predictive Modeling SOA Spring Health Meeting May 28, 2008 Jonathan Polon FSA www.claimanalytics.com.

Slides:



Advertisements
Similar presentations
Barry Senensky FSA FCIA MAAA Overview of Claim Scoring November 6, 2008.
Advertisements

Disability Insurance Predictive Modeling Applications July 30, 2009 Claim Analytics Inc. Jonathan Polon FSA
Data Mining Methodology 1. Why have a Methodology  Don’t want to learn things that aren’t true May not represent any underlying reality ○ Spurious correlation.
Improving Disability Claims Management with Predictive Modeling May 15, 2008 Claim Analytics Inc. Barry Senensky FSA FCIA MAAA Jonathan Polon FSA
Hazırlayan NEURAL NETWORKS Least Squares Estimation PROF. DR. YUSUF OYSAL.
© 2014 Fair Isaac Corporation. Confidential. This presentation is provided for the recipient only and cannot be reproduced or shared without Fair Isaac.
Disability Pricing and Dental Fraud Detection: Supervised and Unsupervised Learning October 11, 2007 Jonathan Polon FSA Claim Analytics Inc.
Dental Insurance Fraud Detection With Predictive Modeling SOA Spring Health Meeting May 29, 2008 Jonathan Polon FSA
Computer vision: models, learning and inference
Predictive Modeling for Disability Pricing May 13, 2009 Claim Analytics Inc. Barry Senensky FSA FCIA MAAA Jonathan Polon FSA
The loss function, the normal equation,
Discovery Through Statistics Claim Analytics Group Disability Reserving Unleash the Power of the 21 st Century Canadian Institute of Actuaries June 29.
Engineering Data Analysis & Modeling Practical Solutions to Practical Problems Dr. James McNames Biomedical Signal Processing Laboratory Electrical & Computer.
1/55 EF 507 QUANTITATIVE METHODS FOR ECONOMICS AND FINANCE FALL 2008 Chapter 10 Hypothesis Testing.
The generalized Additive Nonparametric GARCH Model --With application to the Chinese stock market Ai Jun Hou Department of Economics School of Economics.
Data Mining CS 341, Spring 2007 Lecture 4: Data Mining Techniques (I)
Data Mining: A Closer Look Chapter Data Mining Strategies (p35) Moh!
Copyright © 2010 Pearson Education, Inc. Publishing as Prentice Hall Statistics for Business and Economics 7 th Edition Chapter 9 Hypothesis Testing: Single.
Sparse vs. Ensemble Approaches to Supervised Learning
Part I: Classification and Bayesian Learning
Predictive Modeling and Dental Fraud Detection November 22, 2007 Jonathan Polon FSA Barry Senensky FSA FCIA MAAA Claim Analytics Inc.
Neural Networks And Its Applications By Dr. Surya Chitra.
Enterprise systems infrastructure and architecture DT211 4
Data Mining By Andrie Suherman. Agenda Introduction Major Elements Steps/ Processes Tools used for data mining Advantages and Disadvantages.
Walter Hop Web-shop Order Prediction Using Machine Learning Master’s Thesis Computational Economics.
Data Mining Techniques
Risk Adjustment Data For Business Insight Health Care Service Corporation September 2012.
Chapter 10 Hypothesis Testing
Fundamentals of Hypothesis Testing: One-Sample Tests
Chapter 8 Introduction to Hypothesis Testing
CS490D: Introduction to Data Mining Prof. Chris Clifton April 14, 2004 Fraud and Misuse Detection.
Data Mining and Application Part 1: Data Mining Fundamentals Part 2: Tools for Knowledge Discovery Part 3: Advanced Data Mining Techniques Part 4: Intelligent.
COMP3503 Intro to Inductive Modeling
Machine Learning1 Machine Learning: Summary Greg Grudic CSCI-4830.
Chapter 10 Hypothesis Testing
1 Introduction to Hypothesis Testing. 2 What is a Hypothesis? A hypothesis is a claim A hypothesis is a claim (assumption) about a population parameter:
Chapter 7 Neural Networks in Data Mining Automatic Model Building (Machine Learning) Artificial Intelligence.
Neural Networks for Protein Structure Prediction Brown, JMB 1999 CS 466 Saurabh Sinha.
1999 CASUALTY LOSS RESERVE SEMINAR Intermediate Track II - Techniques
The CRISP Data Mining Process. August 28, 2004Data Mining2 The Data Mining Process Business understanding Data evaluation Data preparation Modeling Evaluation.
Chap 8-1 A Course In Business Statistics, 4th © 2006 Prentice-Hall, Inc. A Course In Business Statistics 4 th Edition Chapter 8 Introduction to Hypothesis.
Chap 8-1 Fundamentals of Hypothesis Testing: One-Sample Tests.
1 Some more examples Client satisfaction Products sold Trusted advisor score Net growth TOP PERFORMERS Age diversity HIGH Credibility HIGH Absenteeism.
Data Mining and Decision Support
CIA Annual Meeting LOOKING BACK…focused on the future.
Cheng-Lung Huang Mu-Chen Chen Chieh-Jen Wang
PROPRIETARY  2003 Data Research Analysis & Consultancy Solutions All Rights Reserved. This is achieved by: Improving availability / reducing stock outs.
Financial Data mining and Tools CSCI 4333 Presentation Group 6 Date10th November 2003.
An Effective Hybridized Classifier for Breast Cancer Diagnosis DISHANT MITTAL, DEV GAURAV & SANJIBAN SEKHAR ROY VIT University, India.
Clustering Algorithms Minimize distance But to Centers of Groups.
Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall Statistics for Business and Economics 8 th Edition Chapter 9 Hypothesis Testing: Single.
Neural networks (2) Reminder Avoiding overfitting Deep neural network Brief summary of supervised learning methods.
2006 General Meeting Assemblée générale 2006 Chicago, Illinois IP 13 LTD Denis Garand 2006 General Meeting Assemblée générale 2006.
Combining Models Foundations of Algorithms and Machine Learning (CS60020), IIT KGP, 2017: Indrajit Bhattacharya.
Data Mining, Machine Learning, Data Analysis, etc. scikit-learn
Machine Learning with Spark MLlib
Data Based Decision Making
Discussion/Presentation of Park and Basu: “Alternative Evaluation Metrics for Risk Adjustment Models” Stephen P. Ryan, Olin.
Machine Learning for dotNET Developer Bahrudin Hrnjica, MVP
2006 General Meeting Assemblée générale 2006 Chicago, Illinois
Dr. Morgan C. Wang Department of Statistics
Machine Learning Interpretability
Overfitting and Underfitting
Data Mining, Machine Learning, Data Analysis, etc. scikit-learn
The loss function, the normal equation,
Data Mining, Machine Learning, Data Analysis, etc. scikit-learn
Mathematical Foundations of BME Reza Shadmehr
Linear Discrimination
Regression and Clinical prediction models
Presentation transcript:

Evaluating Inforce Blocks Of Disability Business With Predictive Modeling SOA Spring Health Meeting May 28, 2008 Jonathan Polon FSA

Intro to Predictive Modeling Modeling Diagnoses Evaluation of Inforce Blocks Valuation of Open Claims Claims Management Opportunities Summary Agenda

Introduction to Predictive Modeling

A Part of Everyday Life Have you used a predictive model today? Mail sorting Credit card processing Credit scores Weather forecasting Grocery shopping

What is Predictive Modeling Harnesses power of modern computers to find hidden patterns in data Used extensively in industry Many possible uses in insurance: Claim management Pricing Reserving Fraud detection

About Predictive Models May be parametric… apply numerical methods to optimize parameters E.g., gradient descent, competitive learning Or non-parametric often have a decision tree form typically optimized using exhaustive search

Predictive Modeling Tools Some common techniques Generalized linear models Neural networks Genetic algorithms Random forests Stochastic gradient boosted trees Support vector machines

Modeling Diagnoses

Why Model Diagnoses? There’s more to diagnosis than category within categories, severity varies similarities can exist between diagnoses of different categories how do we extract more information?

Scoring Diagnoses Create a series of metrics for each diagnosis relative values from for example: -terminal- curable -fine motor skills- pharmaceuticals allows every diagnosis to be compared to every other diagnosis

Scoring Diagnoses - Example ALS OA/Hand DDD DX CatNervous System Musculo- Skeletal Terminal1000 Curable037 Fine Motor1080

Benefits Modeling allows each diagnosis to be compared to every other diagnosis Similarities and differences can be found and quantified – both within categories and between categories Better information  better predictions

Evaluation of Inforce Blocks With Predictive Modeling

Inforce Blocks of Business Two predictive modeling applications: Valuation of open claims with claimant-specific termination rate assumptions Identifying claim management opportunities

Claimant-specific Termination Rate Assumptions

Current Termination Rates Table-based Use small subset of known information: - Age- Gender - EP- Maybe occ or diag Tables work well in low dimensions In high dimensions, tables are often sparsely populated

Better Termination Rates Predictive modeling allows several additional factors to be accounted for: Primary, secondary and tertiary diagnosis Industry / SIC Code Pre-disability income Monthly benefit Own occ period Reporting lag And more…

Modeling Termination Rates Build models to predict likelihood of termination between several horizons, eg: 0-3 months 3-6 months 6-12 months… Interpolate between key points Beyond 36 or 48 months, blend into table Too few terminations to model

It starts with a data extract: - Age- EP - Gender- Diagnosis - 2nd diagnosis- Income - Benefit- Occupation - Region- Own occ period - Industry- and more Building the Model

1.Model presented with your historic claim data, including known outcomes. 2.Model begins making predictions on cases in the sample… 3.…compares predictions to real outcomes, and begins to detect patterns… Initial predictions are rough…

But… model continues to learn With each iteration the model’s accuracy improves And converges to a complex algorithm that fits the experience

Model Validation Critical test of model’s accuracy For 10% of data, withhold from modeling For this data, compare model predictions to actual outcomes

Validation Results

Benefits Reserves are not averages – they are appropriate for each claim –Important if open claims differ from historical Model can train using data from either target company or acquiring company –To reflect claims management practices that will be used going forward

Identifying Claims Management Opportunities

Claims Management Practices Can vary greatly between companies In an acquisition scenario, claims area may need to quickly review inforce claims Predictive modeling can provide guidance about opportunities for inforce claims

Profile of Older Claims

Claims Open More than 2 Yrs About 5% of older claims had high probability of termination when new It may be possible to revisit and help many of these claimants to return to work Most older claims had low probability of termination at benefit commencement date Probability of termination likely even lower now It may be possible to review and reduce allocation of resources to these claims (e.g., rehab)

Benefits Predictive model accurately accounts for the unique characteristics of each claim Predictive modeling isolates opportunities to realize significant value within the open claims block

Summary

Summary Claimant-specific termination rates can be modeled for inforce blocks of DI business – More accurate valuation of open claims – Identification of opportunities to realize value via claims management

Questions?