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

Transcription

1 Practical applications of Predictive Modelling Overview of the process, the techniques and the applications Jean-Yves Rioux CIA 2014 Annual Meeting, June 19, 2014

2 Agenda 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 1

3 What it is 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 Why do it Drivers Capabilities (Cost of storage, computer processing speed and memory) Data availability and appetite in learning more about own data Competition is doing it 2

4 Process overview How it needs to evolve Hindsight Insight Foresight Predictive and prescriptive Descriptive Optimization algorithms Simulation and modelling Quantitative analyses Advanced forecasting Role-based performance metrics Exceptions and alerts Slice and dice queries and drill-downs Management reporting Enterprise data management 3

5 Process overview The process of delivering business analytics results is one of continuous improvement Issues What business problem are you trying to solve? Actions Applied analytics Business Results Facts What data can be leveraged to understand the business and improve performance? How do we look to the further and build analytic insights directly into business processes? Understanding What is currently happening or has happened related to our business and why? What should we do about it? 4

6 Data proliferation 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 5

7 Sources of data internal There is increased interest in understanding own data Administration system(s) Representative data categories Policyholder info Past/current claims Experience results Claims system(s) Age Gender Smoking status Date incurred Date reported Amount Incidence Lapse Termination Amount inforce Cause Mortality Reinsurance system(s) Province of residence Occupational code Persistency Reinsurance info Product design Asset info Asset system(s) Premium Rates Allowances Surrender rights/charges Vesting Coupon rates Notional amounts Coverage Market value adj. Market values Renewal rights Experience study process Rollover/reset rights Conversion rights

8 Sources of data external Companies who are succeeding in advanced analytic analysis are doing so by their commitment to exploring new data 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 Representative data categories Economic Real Estate Equities Commodities Interest Rates Foreign Exchange Inflation Economic/Bus. trends National indices Wage Data Wealth/Net Worth Unemployment Stats Aggregate CRA Data Geographic Crime Statistics Climate Data Geographic Mapping Population Concentration Demographic Age Gender Ethnicity Income Immigration Data DB, LB and health Death Diabetes Cancer Cardiovascular Disability Injury Depression/Mental Behaviors & lifestyle Physical Activity Level Hobbies Lifestyle Clusters Social Values Financial Credit Score Gross/Total Debt Service Ratio Credit Ratings Medical & drugs Disability Data Hospital Directory Nursing Home Data Hospital Visit Statistics Prescription Drug Usage Physician Data Purchase behaviors Purchase Propensities Spend by Category Purchase Triggers Competitive data Premium Rates Crediting Rates Guaranteed Rates Product Features 7

9 Visualization 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 Source: A business guide to visual communication, Visage, A Column Five Company 8

10 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 9

11 Life and health insurance applications 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 Claims Predict claim frequency and severity Prioritize resources Identify likely fraudulent/rescinded claims Pricing/reserving Improve pricing accuracy Project impact of deviations from pricing parameters Reserve more accurately Underwriting Identify best risks and prioritize acceptation efforts Identify applicants for whom additional underwriting is needed Support simplified underwriting In-force management Identify and retain policyholders likely to surrender Offer additional products to current customers Profile customers Experience analysis Identify experience drivers Handle low credibility data by enhancing the data Create own mortality/lapse tables 10

12 Forecasting techniques Category Prep for analysis/steps applicable to any method Traditional statistical techniques Skills/techniques Model validation Data validation/cleaning Ordinary least squares Logistic regression Generalized linear model Time series Survival/failure time analysis Methods that group/organize Other methods Trees/clustering Factor analysis/principal component analysis Neural networks Bayesian networks 11

13 Forecasting techniques 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) Time series Credit-based insurance scores, ratemaking, and underwriting Trending of investment behaviour or of costs and prices through time 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. 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 12

14 Forecasting techniques 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.) 13

15 Forecasting techniques (nice to have) 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 14

16 Conclusion 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 15

17 Deloitte, one of Canada's leading professional services firms, provides audit, tax, consulting, and financial advisory services. Deloitte LLP, an Ontario limited liability partnership, is the Canadian member firm of Deloitte Touche Tohmatsu Limited. Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms.