This application relates to apparatus and methods for identifying fraudulent transactions. In some examples, a computing device generates a decision matrix to identify fraudulent transactions. To generate the decision matrix, the computing device may determine scores for a plurality of transactions, and may determine transaction categories for each transaction based on the scores. The computing device may also determine a number of predictable features based on applying machine learning techniques to the transactions. A risk category is then determined for the number of predictable features. The computing device generates the decision matrix based on the transaction categories and the risk categories. In some examples, the computing device applies the generated decision matrix to an ongoing purchase transaction to determine if the ongoing purchase transaction is fraudulent. In some examples, the computing device prevents completion of the purchase transaction if the purchase transaction is determined to be fraudulent.