# How to detect a no good person in data?

I am struggling to figure a way to determine if a person is 'no good' or 'good'.

A little bit about what I am trying to accomplish, I have a data set of a payment for a violation with a check or a bounced check on a given day, if there is a bounced check it means there is no such account.

If they have a pattern of payments and bounces in chronological order then they are doing something fishy such as gaming the system. But, if they have payments, bounces, payments, and little to no bounces after that then the initial bounces could have been human entry errors.

I want to use the data set to determine who are the 'no good' people that have a pattern, ex. paying, bouncing, paying, bouncing.

Here is an example of my data set:

person     state date                           status  bounce_code
A           NY  23-DEC-15 06.27.08.000000000 PM Paid
A           NY  23-DEC-15 06.12.58.000000000 PM Paid
A           NY  18-DEC-15 10.14.39.000000000 AM Return  R03
A           NY  15-DEC-15 04.16.58.000000000 PM Return  R03


The original data set contains thousands of people and their history of payments and bounces.

One idea is to analyze each person to see if they fit a "mold" (payment, bounce, payment, bounce) and if they do i will label them as a 'no good' person.

Please let me know if this is confusing and I am open to suggestions on how to approach this problem.

• What are the different bounce codes? What are the english descriptions for them? You might have to "weigh" certain bounce codes more heavily than others. – Dwayne Hinterlang Apr 7 '16 at 15:12
• Ppl still use cheques? =l – Nathan Apr 8 '16 at 21:46