I'm curious what the differences are and if there are any examples of how a credit model algorithm works.

I suspect this has been talked about various times in the past but I have not been around here very long.

It seems that filtering is pretty straightforward to understand but can be so restrictive it limits the number of notes you can get. Everything is AND'd together and all must be true. Consider "Public Records" ... if you filter for the ideal of 0 then you may miss notes that have 1 but it happened 60 months ago (5 yrs). Is that length of time enough to show whatever lead them to that is no long an issue?

I have never read any books on credit scoring but simplistically I believe it assigns points/weights to each credit attribute you care about (Public Records, Employment Len, Months since Delq, Nbr Accounts open, Credit Utilz %, % of Bankcards >75% of Limit, Earliest Credit Line (Credit Age), etc). Bad example below but gets basic point across.

PubRecs = 0 then 75

PubRecs 1-5 then 35

PubRecs 6-11 then 25

PubRecs 12-23 then 15

PubRecs 24+ then 0

Then you add up the values of all the points/weights to see if it passes a threshold you set. Or perhaps multiple thresholds.

If Total < 450, then do not invest

If Total between 450 and 475, then invest $25

If Total between 475 and 500, then invest $50

If Total > 500, then invest $75

Credit scoring will let you find more notes that meet your guidelines.

(Variation to point system above could be opposition so the smaller the number the better).