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Predictive Modelling Applied to Propensity to Buy Personal Accidents Insurance Products

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Predictive Modelling Applied to Propensity to Buy Personal Accidents Insurance Products Predictive Modelling Applied to Propensity to Buy Personal Accidents Insurance Products Esdras Christo Moura dos Santos Internship report presented as partial requirement for obtaining the Master’s degree in Advanced Analytics i 2017 Title: Predictive Models Applied to Propensity to Buy Personal Accidents Student: Esdras Christo Moura dos Santos Insurance Products MAA i i NOVA Information Management School Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa PREDICTIVE MODELLING APPLIED TO PROPENSITY TO BUY PERSONAL ACCIDENTS INSURANCE PRODUCTS by Esdras Christo Moura dos Santos Internship report presented as partial requirement for obtaining the Master’s degree in Advanced Analytics Advisor: Mauro Castelli ii February 2018 DEDICATION Dedicated to my beloved family. iii ACKNOWLEDGEMENTS I would like to express my gratitude to my supervisor, Professor Mauro Castelli of Information Management School of Universidade Nova de Lisboa for all the mentoring and assistance. I also want to show my gratitude for the data mining team at Ocidental, Magdalena Neate and Franklin Minang. I deeply appreciate all the guidance, patience and support during this project. iv ABSTRACT Predictive models have been largely used in organizational scenarios with the increasing popularity of machine learning. They play a fundamental role in the support of customer acquisition in marketing campaigns. This report describes the development of a propensity to buy model for personal accident insurance products. The entire process from business understanding to the deployment of the final model is analyzed with the objective of linking the theory to practice. KEYWORDS Predictive models; data mining; supervised learning; propensity to buy; logistic regression; decision trees; artificial neural networks; ensemble models. v INDEX 1. Introduction AND Motivation ........................................................................................ 1 2. Part I.............................................................................................................................. 2 2.1. Data Mining Processes .......................................................................................... 2 2.1.1. CRISP-DM ........................................................................................................ 2 2.1.2. SEMMA ........................................................................................................... 4 2.2. Predictive Models .................................................................................................. 6 2.2.1. Logistic Regression ......................................................................................... 7 2.2.2. Decision Trees ................................................................................................ 9 2.2.3. Artificial Neural Networks ............................................................................ 13 2.2.4. Ensemble Models ......................................................................................... 16 2.3. Predictive Models Evaluation .............................................................................. 17 2.3.1. Performance Measure of Binary Classification ............................................ 17 3. Part II........................................................................................................................... 24 3.1. Methodology ....................................................................................................... 24 3.1.1. Business Understanding ............................................................................... 24 3.1.2. Data Understanding ..................................................................................... 25 3.1.3. Data Preparation .......................................................................................... 26 3.1.4. Modelling ...................................................................................................... 31 3.1.5. Final Evaluation and Results ......................................................................... 45 4. Conclusions and Deployment ..................................................................................... 50 4.1. Limitations and Recommendations for Future Works ........................................ 50 Appendix.......................................................................................................................... 52 Bibliography..................................................................................................................... 77 vi LIST OF FIGURES Figure 1 - CRISP-DM ................................................................................................................... 3 Figure 2 - SEMMA ....................................................................................................................... 4 Figure 3 – Sigmoid Function. ...................................................................................................... 8 Figure 4 – Decision Tree Representation. .................................................................................. 9 Figure 5 – Logworth function. .................................................................................................. 11 Figure 6 – Entropy of a Binary Variable .................................................................................... 12 Figure 7 - Artificial Neural Network Representation ............................................................... 13 Figure 8 – Sigmoid Activation Function. ................................................................................... 15 Figure 9 – ROC Curve ................................................................................................................ 22 Figure 10 - Lift Chart ................................................................................................................. 23 Figure 11 – Distribution of Idade_Adj ...................................................................................... 27 Figure 12 – Distribution of No_Claims_Ever_NH ..................................................................... 28 Figure 13 – Sample Distribution of Idade-Adj .......................................................................... 29 Figure 14 – Correlation Matrix ................................................................................................. 30 Figure 15 – Modelling Process. ................................................................................................ 32 Figure 16 - Regression Models ................................................................................................. 33 Figure 17 – Regression model Average Squared Error ............................................................ 34 Figure 18 – Regression ROC Curve ........................................................................................... 35 Figure 19 – Regression Misclassification Rate ......................................................................... 36 Figure 20 – Decision Tree Models ............................................................................................ 36 Figure 21 – Decision Tree Average Squared Error ................................................................... 38 Figure 22 – Decision Tree Misclassification Rate ..................................................................... 39 Figure 23 – Decision Tree ROC curves. ..................................................................................... 39 vii Figure 24 – Decision Tree Structure ......................................................................................... 40 Figure 25 – Artificial Neural Networks Models ........................................................................ 41 Figure 26 – Artificial Neural Network ASE with all inputs. ....................................................... 41 Figure 27 – Artificial Neural Network Average Squared Error. ................................................ 42 Figure 28 – Artificial Neural Network Misclassification Rate. .................................................. 42 Figure 29 – Artificial Neural Network ROC curves. .................................................................. 43 Figure 30 – Posterior Probabilities ........................................................................................... 44 Figure 31 – Ensemble Model ROC Curves ................................................................................ 45 Figure 32 – Cummulative Lift Comparison ............................................................................... 46 Figure 33 – Histogram of Unadjusted Probabilities. ................................................................ 48 Figure 34 – Histogram of Adjusted Probabilities. .................................................................... 48 Figure 35 – Decision Tree Structure. ........................................................................................ 76 viii LIST OF TABLES Table 1 – CRISP-DM & SEMMA .................................................................................................. 5 Table 2 – Confusion Matrix ...................................................................................................... 17 Table 3 – Data Partition. ........................................................................................................... 29 Table 4 – Regression Model Coefficients. ................................................................................ 34 Table
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