# Is there a way to use debt details without aggregating them to predict the probability of payment of every debt per debtor?

I edited my post for clarity, for the second time. Thx lpounng for the feedback.

I am seeking advice on predicting debt payment within a year. Each debt has its own carachteristic wich are not easy to aggregate.

Moreover, multiple debts can correspond to the same debtor, and it is common for a debtor to fail to pay multiple debts. So If I tried to calculate the probability of payment for individual debts and multiply them to get the probability of payment for all debts of a debtor I would be wrong because the debts are not independent (Bayes).

I suspect that, when the time comes to predict a new value, to predict payment for a new debtor, I need to have information about all of their debts at the time. This leads me to construct a register where all information about debts for a debtor needs to be contained... so how could I go about it? I don´t see possible to make a column for each feature for each of the n possible debts per debtor (i.e. "amount paid debt 1", "amount paid debt 2", ... , "amount paid debt n".) because of NA values that would populate most columns and that I can´t know n in advance (maybe a new debtor arrives with n+1 debts... and how would I go about imputing values for that load of variables). On the other hand if I aggregate the amount paid for every debt I would loose information (not that much in this feature... but think more of number of installments paid... which would be kind of heterogeneous when summed up... like summing apples and bananas).

So... is it possible to use as features the individual debts fields or I should make a model doing feature engineering predictors that summarise total debt carachteristics?

Thank you

• I read the post 2 times and still not getting what OP is asking for - the title does not match the body, objective not well defined, and multiple questions are raised. My advice is to take a step back, clear up your mind on what the objective is first Commented Feb 23, 2023 at 3:05
• Thank you lpounng for your feedback and your time. Commented Feb 23, 2023 at 12:14