I would like to create a behavioral credit scoring model to score the applications for which transaction data is available. There's an obvious problem mentioned in Thomas et al. Credit Scoring and Its Applications (link) — "Someone who opened an account within the last six months cannot have an average balance (e.g.)for the last year or the last half-year".
Therefore I'm going to slice the history for sequential time points and reweight the slice observations for old and new customers. What's the best way to do this?
Also any literature on creating behavioral models based on logistic regression is appropriate.