I am tasked with analyzing data to find "triggers" to an event. Specifically, this is transaction data from a bank (e.g., checking account daily balances, daily over draft fees, daily number of checks cleared etc) and the event of interest is the checking account being closed by the customer. It sounds like I am really needing to do a feature selection (important features might be something like "having three over drafts in 6 months").
I was thinking about cox regression (possibly with time varying covariates) and some variable selection (the paper here was an inspiration). In this way significant variables could be considered triggers. The business will be using these triggers in monitoring software (if a trigger event is detected), flag the customer as an attrition risk, and then do something to keep them from leaving.
- Does this seem like the correct approach or are there others?
It seems like it will be important to construct variables to determine the best triggers. For example, is the best trigger:
a. Number of overdrafts in 3 months?
b. Number of overdrafts in last 7 days?
c. 3 or more overdrafts in the past 2 months
Any suggestions on how to determine these triggers?