A pay-later solution like Simpl comes with risk - some customers don’t pay their bill on time. When this happens, our collections team calls them up and gently reminds them that their bill is due. Some people even try to vanish - they ghost us - without paying their bill, resulting in escalation to our skip trace team.

In this talk I’ll go over how we use survival models to optimize our calling team by deciding who has skipped (and needs a trace), who should get a gentle reminder, and in what order of priority.

Outline

TL;DR

This talk is about using survival models to optimize the process of making collection calls (“dear sir, please pay your bill, it’s overdue”).

Context:

• An overview of how the calling process is structured. This will give an understanding of what we’re trying to optimize.
• Discuss why moving people from one level of the process to another automatically and optimally is important for recovering money.
• Get an understanding of why data-backed decisions are important for overall efficiency. Is it worth it to make 7 calls per user or should you escalate after 4 calls?
• Understand how using panel data for user behavior is significantly different from more standard classifiers which use cross-sectional data.

A brief introduction to survival models:

• What survival models are, and where they are traditionally used. Get an introduction to basic terminology like survival function, hazard rate, censoring, etc.
• Take a look at non-traditional applications of survival models in fields like sales lead prioritization, marketing automation, etc.

How we use survival models:

• How math concepts are directly relevant to the business - a hazard function is directly useful as a lead score, while a survival function tells us who the ghosts are. Math => business decisions.
• Constructing hazard curves via parametric (Weibull) and non-parametric (Kaplan-Meier) and connecting them to our real data.
• Cox proportional model
• Data limitations force us to use censored models.
• Take a look at productionizing these models; how to use this information to make better decisions. One model can solve many problems (escalation, lead scoring, write-off, etc.)

Requirements

This talk is accessible to those with some prior experience in statistics and/or machine learning

Speaker bio

Fasih is a data scientist at Simpl, India’s top pay later platform. When he’s not busy playing video games, he’s busy writing about all-things-Bayes and functional programming. Prefers adrak-wali-chai over coffee, suggests ordering from Tata Cha over Chai Point, and paying using Simpl.

Links

• Implementing TSum: An Algorithm for Table Summarization - http://fasihkhatib.com/2018/10/21/Implementing-TSum-An-Algorithm-for-Table-Summarization
• Frequentism vs Bayesianism - http://fasihkhatib.com/2019/05/10/The-Machine-Learning-Notebook-Frequentism-vs-Bayesianism/

Slides

https://docs.google.com/presentation/d/1qnMPPi3caa_9y1pShqxf7mbEbJsk33fpEgYX6-WDQ0Y/edit?usp=sharing