Detecting fraudulent transactions

A system comprising:

• one or more computer processors;
• and
• one or more computer-readable mediums storing instructions that, when executed by the one or more computer processors, cause the system to perform operations comprising:
• generating at least a first feature vector and a second feature vector representing a first transaction, the first feature vector being different than the second feature vector;
• generating a first sequence vector representing a sequence of transactions associated with the first transaction, the sequence of transactions including the first transaction and at least one transaction preceding the first transaction;
• providing the first feature vector as input into a first machine learning model, resulting in a first feature output value;
• providing the second feature vector as input into a second machine learning model, resulting in a second feature output value, the second machine learning model being different than the first machine learning model;
• providing the first sequence vector as input into an events sequence model, resulting in a first sequence output value;
• providing, a first cumulative input into a secondary machine learning model, resulting in a first cumulative probability value indicating a likelihood that the first transaction is fraudulent, wherein the first cumulative input is generated based on the first feature output value, the second feature output value, and the first sequence output value;
• and
• determining whether the first transaction is fraudulent based on a comparison of the first cumulative probability value to a first threshold probability value.

A non-transitory computer-readable medium storing instructions that, when executed by one or more computer processors of one or more computing devices, cause the one or more computing devices to perform operations comprising:

• generating at least a first feature vector and a second feature vector representing a first transaction, the first feature vector being different than the second feature vector;
• generating a first sequence vector representing a sequence of transactions associated with the first transaction, the sequence of transactions including the first transaction and at least one transaction preceding the first transaction;
• providing the first feature vector as input into a first machine learning model, resulting in a first feature output value;
• providing the second feature vector as input into a second machine model, resulting in a second feature output value, the second machine learning model being different than the first machine learning model;
• providing the first sequence vector as input into an events sequence model, resulting in a first sequence output value;
• providing, a first cumulative input into a secondary machine learning model, resulting in a first cumulative probability value indicating a likelihood that the first transaction is fraudulent, wherein the first cumulative input is generated based on the first feature output value, the second feature output value, and the first sequence output value;
• and
• determining whether the first transaction is fraudulent based on a comparison of the first cumulative probability value to a first threshold probability value.