How to detect a no good person in data?
I would start by looking at the distribution of bounce rates for each individual. That distribution would provide some details around what could be considered a "normal" bounce rate, and where you would draw a line for an extreme bounce rate that implies a 'bad'. Additionally, you can also look at average bounce rates (divide bounce rate by the tenure), and review these averages by tenure. This may reveal that different threshold values might be appropriate for customers with different tenure. For instance, if a customer is new (first month of activity) then even a moderate bounce rate could imply a 'bad'.

Additional metrics -- like the ratio of bounce to valid payments, time between bounce -- could be useful to look at as well.

Once a set of initial thresholds are identified, I would closely review some of the customers who are identified as 'bad' based on those thresholds to ensure that there are not too many false positives. The business point of view (from those who would be using this information to make decisions) would be great to incorporate into this review.