Which attributes to consider for credit card fraud detection on an ATM? I am working on a project in which I am supposed to come up with a system that detects fraud from a credit card transaction. I read about techniques that others have used and decided to use Artificial Neural Networks for their adaptability.

WHAT THE SYSTEM MUST DO

My system is supposed to detect fraud on a credit card used on an ATM, that is, I am assuming that the user has already been granted access to the account by entering their PIN on an ATM. In other words, I am saying before the transaction is authorized, pass it through the fraud detection system so  we can check if it is fraudulent or not.

ATTRIBUTES THAT I AM THINKING TO CONSIDER 


The amount of money to be withdrawn  - here I am assuming that each
user has a certain range of money that they usually withdraws; any
deviations are treated a suspicious.
Time taken to input PIN - here I am thinking that each user has a range of time that they usually take to input PIN; any deviation is treated as suspicious.


Questions


Which attributes can I take into consideration that enables me tell whether a  transaction is fraudulent or not?
What others have considered to solve this problem?
What do you recommend me?
What can be inputs to my Neural network?


Thanks for your ideas.
Location based trends
Number of transactions from a particular ATM in a particular location, compared to the home area. So if the user usually uses a small set of ATMs near to their address, and then suddenly there are 5 large transactions from an ATM on the other side of the country.
Another little suggestion
If it was up to me, I would use a system where the attempted transaction is given a suspiciousness rating, where each factor is given a score and then the overall decision whether or not to authorize the transaction is based on the average score. This would also allow you to prioritize different factors.
Does that make sense? (Sorry, it's my first post, I'm still getting used to the etiquette)