| dc.description.abstract | Identity theft in online banking can cause significant economic and psychological damage to the victims. Traditionally, there has been a strong focus on detecting and hindering fraud, but not nearly as much focus on any identity thefts that may have been involved in these fraud cases. This is beginning to change, but we still know very little about how identity theft happens in online banking, or how to stop it. We have, together with Sbanken, looked closely at a set of reported identity theft cases. Statistics about these cases have been analyzed to see if some subsets of the population are more at risk. Starting with 10 hypotheses, we have worked together to make several functions that should warn of possible identity thefts in the bank. Our main hypothesis is that it is possible to detect identity theft by analyzing the meta- data of each individual user account. Identity theft happens to individuals, and every case might seem like it is unique. Still, we have found some shared patterns between several of the cases, which can be used in detecting future occurrences of identity theft. Our new functions will need further testing on the customers in a bank in order to measure their full effectiveness. Nonetheless, the process has been started, and we have some preliminary findings that indicate that our hypothesis is correct. | en_US |
