Credit card fraudsters are always changing their behavior, developing new tactics. For banks, the damage isn’t just financial; their reputations are also on the line. So how do banks stay ahead of the crooks? For many, detection algorithms are essential.
Given enough data, a supervised machine learning model can learn to detect fraud in new credit card applications. This model will give each application a score — typically between 0 and 1 — to indicate the likelihood that it’s fraudulent. The banks can then set a threshold for which they regard an application as fraudulent or not — typically that threshold will enable the bank to keep false positives and false negatives at a level it finds acceptable.
False positives are the genuine applications that have been mistaken as fraud; false negatives are the fraudulent applications that are missed. Each false negative case has a direct financial impact on a bank as it corresponds to a financial loss. False positives are a little trickier to map to a financial figure since they represent the opportunity cost of losing a customer.
Given the impact fraudulent activity can have, it is vital to have an effective detection system. With fraudsters changing behavior constantly, a detection system is only effective if it can match this rate of change. This is the problem that we were helping with at one of our clients.
A lengthy process
For the past few years, we’ve worked with a global financial institution, on a variety of projects including one to help improve their credit card application fraud detection...