  Credit and debit cards, rather than actual money, have become the universal
payment means. With these cards, it has become possible to buy expensive items
easily without an additional complex authentication procedure being conducted.
However, card transaction features are targeted by criminals seeking to use a
lost or stolen card and looking for a chance to replicate it. Accidents,
whether caused by the negligence of users or not, that lead to a transaction
being performed by a criminal rather than the authorized card user should be
prevented. Therefore, card companies are providing their clients with a variety
of policies and standards to cover this eventuality. Card companies must
therefore be able to distinguish between the rightful user and illegal users
according to these standards in order to minimize damage resulting from
unauthorized transactions.
  However, there is a limit to applying the same fixed standards to all card
users, since the transaction patterns of people differ and even individuals'
transaction patterns may change frequently due to changes income and
consumption preference. Therefore, when only a specific threshold is applied,
it is difficult to distinguish a fraudulent card transaction from a legitimate
one.
  In this paper, we present methods for learning the individual patterns of a
card user's transaction amount and the region in which he or she uses the card,
for a given period, and for determining whether the specified transaction is
allowable in accordance with these learned user transaction patterns. Then, we
classify legitimate transactions and fraudulent transactions by setting
thresholds based on the learned individual patterns.
