Practical applications of
Predictive Modelling
Jean-Yves Rioux
CIA 2014 Annual Meeting, June 19, 2014
Overview of the process,
the techniques and the
applications
What it is and why do it
2
Process overview
3
Data proliferation
5
Data sources
6
Visualization
8
Life and health insurance applications
10
Forecasting techniques
11
Set of tools and techniques used to describe, predict,
and recommend business courses of action that take into
account consumer, provider, and distributor behavior,
which draws on many disciplines: statistics, modelling,
optimization, clustering, and market research
It relies heavily on vast amounts of data and
computing power
Practical applications of Predictive Modelling
What it is
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Drivers
• Capabilities (Cost of storage, computer processing speed and memory)
• Data availability and appetite in learning more about own data
Competition is doing it
Practical applications of Predictive Modelling
Process overview – How it needs to evolve
Hindsi
ght
Ins
ight
Fore
s
ight Simulation and modelling
Quantitative analyses
Advanced forecasting
Exceptions and alerts
Slice and dice queries and drill-downs
Management reporting Optimization
algorithms
Predictive and prescriptive
Role-based performance metrics
Descriptive
The process of delivering business analytics results is one of continuous improvement
Practical applications of Predictive Modelling
Process overview
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Facts
What data can be leveraged to understand the business and improve performance?
Understanding
What is currently happening or has happened related to our business and why? What should we do about it?
Actions
How do we look to the further and build analytic insights directly into business processes?
Issues
What business problem are you trying to solve?
Applied
analytics
Business
Results
Every day, we create 2.5 quintillion
bytes of data — so much that 90% of
the data in the world today has been
created in the last two years alone
Practical applications of Predictive Modelling
There is increased interest in understanding own data
Practical applications of Predictive Modelling
Sources of data – internal
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Representative data categories
Past/current claims Date incurred Date reported Amount Cause Experience results Incidence Lapse Termination Mortality Persistency Reinsurance info Premium Rates Allowances Coverage Product design Surrender rights/charges Vesting
Market value adj. Renewal rights Rollover/reset rights Conversion rights Asset info Coupon rates Notional amounts Market values Policyholder info Age Gender Smoking status Amount inforce Province of residence Occupational code Administration system(s) Claims system(s) Reinsurance system(s) Asset system(s)
Practical applications of Predictive Modelling
Sources of data – external
Demographic Age Gender Ethnicity Income Immigration Data Financial Credit Score Gross/Total Debt Service Ratio Credit Ratings Geographic Crime Statistics Climate Data Geographic Mapping Population Concentration
Behaviors & lifestyle
Physical Activity Level Hobbies
Lifestyle Clusters Social Values
Purchase behaviors
Purchase Propensities Spend by Category Purchase Triggers
National indices
Wage Data Wealth/Net Worth Unemployment Stats Aggregate CRA Data
DB, LB and health
Death Diabetes Cancer Cardiovascular Disability Injury Depression/Mental
Medical & drugs
Disability Data Hospital Directory Nursing Home Data Hospital Visit Statistics Prescription Drug Usage Physician Data Economic Real Estate Equities Commodities Interest Rates Foreign Exchange Inflation Economic/Bus. trends
Companies who are succeeding in advanced analytic analysis are doing so by their
commitment to exploring new data
Competitive data Premium Rates Crediting Rates Guaranteed Rates Product Features Data providers Acxiom
Agriculture and Agri-Food Canada AM Best
AWCBC Bank of Canada Bloomberg BDC
Canada Hospital Directory CMHC
Canada Revenue Agency Canadian Cancer Society CIHI
CMA Cap Index
Citizenship and Immigration Canada Dun & Bradstreet
HRSDC Environics Equifax Experian GHDx Industry Canada Ipsos
LifeGuide
Natural Resources Canada
Office of the Superintendent of Bankruptcy Canada
OECD PAHO
Public Health Agency of Canada Public Safety Canada Statistics Canada Undata UIS World Bank World Values Survey
Powerful features
• Uses our cognitive abilities
• Ability to include many
dimensions
• Relatively quick to interpret
• Dynamism/ filtering
capabilities
• Data mining/ Drill down
• Linkage between graphs
Practical applications of Predictive Modelling
Visualization
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Practical applications of Predictive Modelling
Visualization
Typical layouts
• Bar/column chart/dot plot
• Stacked bar/column
• Bubble chart
• Area chart
• Line chart
• Pie chart
• Bubble map
• Choropleth map (geographic)
• Treemap
• Dendrogram (hierarchy)
• Boxplot
• Scatterplot
• Network diagram/graph
• Venn diagram
• Heat map
• Arrow Diagram
• Waterfall chart
Sales & marketing
• Identify target groups
• Identify characteristics correlated with purchase decision
• Understand purchase behaviors and recommend the right product
• Recruit agents whose characteristics are similar to successful agents
• Monitor existing agents
Underwriting
• Identify best risks and prioritize acceptation efforts • Identify applicants for whom additional underwriting
is needed
• Support simplified underwriting
Claims
• Predict claim frequency and severity • Prioritize resources
• Identify likely fraudulent/rescinded claims
In-force management
• Identify and retain policyholders likely to surrender • Offer additional products to current customers • Profile customers
Pricing/reserving
• Improve pricing accuracy
• Project impact of deviations from pricing parameters • Reserve more accurately
Experience analysis
• Identify experience drivers
• Handle low credibility data by enhancing the data • Create own mortality/lapse tables
Practical applications of Predictive Modelling
Life and health insurance applications
10 © Deloitte LLP and affiliated entities.
Category Skills/techniques Prep for
analysis/steps applicable to any method
Model validation
Data validation/cleaning
Traditional statistical techniques
Ordinary least squares Logistic regression Generalized linear model Time series
Survival/failure time analysis
Methods that group/organize
Trees/clustering
Factor analysis/principal component analysis
Other methods Neural networks Bayesian networks
Practical applications of Predictive Modelling
Skills/techniques Typical use Definition Ordinary Least
Squares (OLS)
Prediction of the total cost of an insured member
Fundamental model in which the dependent variable is a linear function of explanatory variables
Logistic regression Purchase propensity, churn/lapse propensity, likelihood of response to marketing campaign, fraud detection
Prediction regarding the likelihood of discrete or categorical responses based one or more predictor variables; can deliver insights into which variables are more or less likely to predict event outcome
Generalized Linear Model (GLM)
Credit-based insurance scores, ratemaking, and underwriting
Generalization of OLS regression to allow for the dependent variable to have various distributions, as opposed to
Normal. This is enabled through the use of a “link” function, which expresses the expected dependent variable in terms of a linear combination of the independent variables.
Time series Trending of investment behaviour or of costs and prices through time
Analysis of data that is collected through time in order to uncover patterns and make predictions. The major
difference versus regression models is that the observations are correlated
Practical applications of Predictive Modelling
Forecasting techniques
12 © Deloitte LLP and affiliated entities.
Skills/techniques Typical use Definition Trees/clustering Geographical rating territories,
market segmentation, profitability analysis
The grouping of a set of observations into subsets (clusters) so that observations are similar within a cluster and
dissimilar to observations in other clusters; used to discover structure in data without providing a prior explanation or interpretation
Model validation Used for all models Process of assessing the validity of a model against the underlying data; includes graphical or numerical methods
Data
validation/cleaning
Used for all models • Validation – process of checking the data for accuracy through the use of a set of rules (e.g., checking
formatting, data type, etc.)
• Cleaning – process of identifying and correcting errors made in data entry and processing (e.g., correcting typos, removing duplicates, etc.)
Practical applications of Predictive Modelling
Skills/techniques Typical use Definition Survival/failure
time analysis
Experience studies (e.g., mortality, lapse, etc.)
Model which analyzes the time until the occurrence of a specified event (such as death)
Factor analysis/ Principal Component Analysis
Analysis of relationships among factors such as survey items or test scores, reduction of the number of variables in a model
Methods used to reduce a set of observable, correlated variables to a smaller number of unobserved latent factors
Neural networks Portfolio selection, credit scoring, fraud detection
Techniques modeled after the hypothesized processes of learning in the brain and used to model complex
relationships between inputs and outputs or to find patterns in data
Bayesian networks Identification of key independent and dependent risk variables affecting the onset of disease, decision making & diagnostic models
Probabilistic directed graphical model that represents a set of variables under various states and their dependencies, which are conditioned on these states
Practical applications of Predictive Modelling
Forecasting techniques (nice to have)
14 © Deloitte LLP and affiliated entities.
• Aim at developing a process which can provide foresight, not just hindsight
• Data available will continue to increase
• Visualization, a tool at the heart of predictive modelling, is powerful
• Your efforts or lack thereof will result in a competitive advantage/ disadvantage
Practical applications of Predictive Modelling
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