I have credit card transaction data, that I will describe with a relationship diagram. There are thousands of records, which are tied to a credit card, tied to a customer. Each customer can have 1-2 cards, on which there can be anywhere between 1-30 transactions (unbalanced repeated), ordered by date. Each transaction record also contains a boolean variable classifying if the transaction was tagged as fraud. The goal is to build a model that will be able to predict if a transaction from another set of test data is fraudulent or not.
The way I see it, this is hierarchial data, where [Transactions] are nested within [Credit card] nested within [Customer]. One source of my uncertainty is how to treat each level. I am studying variation in purchasing habits of customers to classify whether it was fraudulent, the results of which would be applicable to other customers, hence customer is a random effect. But the features pertaining to customers are their tier (always 3 levels), and age (20-70 range), so they are fixed effects? Would the credit limit/type and transaction value/sector variables also be fixed effects, since they are specific to each customer. Should I recode numeric variables of customer - age, credit card - limit, and transaction - value into ordered categories so I can treat them as fixed effects as well?
Would a Generalized Linear Mixed Model with transactions as unbalanced repeated measures be appropriate for modelling? What are the fixed and random effects?
Thanks to everyone who reads my question and any input you may have.