Statistical modelling question

[speedy_thrills]

Foremost I apologise if this is in the wrong place to post this question, I did consider the "finance" section but most of those questions pertained to personal finance.

I'm normally a "people manager" (although really I'm just a subject matter expert for a team) but I've been asked to look at a small fixed rate residential loan portfolio with a consistently very high churn rate. Unfortunatly it's come to light that there had been little consideration given to managing the customer churn aspect of the portfolio which we know is poor by external measures (acquisition is excellent, retention is terrible.) We have a range of historic data - all the usual loan data (i.e. amounts, repayment rates etc.) and a few peripherals such as if early exit fees have previously been calculated, if discounts and incentives where offered previously, if a client has been retained using incentives, monthly reporting on how many customers have refinanced to other providers etc.

Every month we generate a report of fixed rate agreements which will be expiring but they are infrequently contacted. What I wanted to do was look towards building a simple model so that we could isolate residential loans coming due that are at a higher probability refinancing to another financial service provider.

What sort of modelling do you think I should be looking into initially? While I studied a STEM at university it was a few years ago now and I did not take higher level statistics courses so your assistance and/or thoughts would be very much appreciated.

[speedy_thrills]

Thanks, I'll investigate both of those and read through that document.

[speedy_thrills]

Logistic regression does look like a good start. After reading a few case-studies it look like kNN is often next step on from that.