What is Survival Model and why it is important?

18 26 Agenda Today What is Survival Model?? September Tuesday 26 What is Survival Model?? Survival Analysis Challenges Censoring: incomplete information Survival Analysis features Non Parametric Model Parametric and semi parametric Models Survival Model Example - BFSI Q&A Today December Thursday 18

Principal Data Scientist Principal Data scientist & Big Data Analytics professional with 10 + years of Advanced Analytics experience providing consultancy services across US, Canada, UK, Europe, Middle East & Australia for different LOBs (Auto Finance, Deposits, Credit Cards, Mortgage, Insurance) Director – Data Science, UpGrad Distinguished member of Leaders Excellence at Harvard Square & Advanced Analytics Expert for Experfy, Harvard Innovation Launch Lab Prior Experience with American Express, GE Money & WNS Masters in Applied Statistics & Informatics (IIT Bombay) speaker Madhukar Kumar Principal Data Scientist

What is Survival Model?? Survival Model is a method which actually predicts the month on month probability of a particular event to occur for a customer It is an important concept which not only tells who but also when the event would occur. Other Modelling techniques can answer the “who” part but not “when” is the event going to occur

Survival Analysis Challenges

Censoring: Incomplete information

Survival Analysis Features

Non Parametric Approach

Parametric and semi parametric models

Cox Proportional Hazard Regression

Post Acquisition Example – Mortgage Customer Survival Model Project Objective To predict the probability of survival of Post Modification Mortgage customers for next 12 months Problem Definition Mortgage customers who had trouble in repayment due to job loss, recession, illness etc. were offered modification in payment, term, rate and bank wanted to predict the probability of survival of those customers for next 12 months Methodology Benefits Delivered Censoring and non-normality of data (i.e. incomplete info for observation) poses the main challenge in using any traditional statistical modeling techniques Dependent variable = # months survived * Censor(0); Censor is a dummy variable(1/0) which =1 if the customer becomes 90+ DPD in 12 Months else =0 Univariate Analysis(categorical variables), A non parametric test Log Rank test of equality used and for (continuous variables), univariate Cox proportional hazard regression was used. Variables were picked if p < 0.2 Stepwise Cox proportional hazard regression was run for all significant numeric variables and dummies for categorical variables 12 month probability of survival generated for each mortgage customer! Survival Model was developed within 4 months timeframe Survival Model actually predicted the default (difference in Actual vs predicted was 2% overall) Customer Attrition reduced by 12% within 1 year timeframe resulting in revenue protection of USD $1.2 MM

Final Survival Models

The estimates of survival are accurate on the development and holdout samples The Average difference b/w Actual and Predicted in the development data is 0.19% The Average difference b/w Actual and Predicted in the development data is 0.15%

Model implementation Survival Equation: S(t) = S0(t) ^ exp(b0 + b1*x1 + b2*x2 + b3*x3 + …...+ bn*xn) The Model output will give the Final survival probability {S(t)} month over month for 12 months How to calculate the survival probability for a new population ?? Pick random 10 customers and put the variable values and survival probability into the above equation Calculate S0(t) for 12 months for all the customers You will realise that the S0(t) is constant for all customers at different time level Pick S0(t) for 1,2,...,12 months Use the above S0(t) and variables and parameter estimates to put into the survival equation You will have 12 different survival equation for 12 months

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