1. What is Survival Model and why it is important?

2. 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

3. 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

4. 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

5. Survival Analysis Challenges

6. Censoring: Incomplete information

7. Survival Analysis Features

8. Non Parametric Approach

9. Parametric and semi parametric models

10. Cox Proportional Hazard Regression

11. 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

12. Final Survival Models

13. 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%

14. 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

15. Thanks
Question & Answers