Iterative processing of transaction information to detect fraud

A system, comprising:

• a communication interface;
• one or more memory devices to store instructions for detecting fraud;
• and
• one or more processors to execute the instructions to:
• receive, via the communication interface, first transaction data for a first transaction associated with a first consumer,
• identify a plurality of first transaction attributes associated with the first transaction data,
• determine, based on the first transaction data, a first fraud probability for the first transaction,
• determine that the first fraud probability corresponds to a first fraud classification, defined by a first set of policies associated with a first set of merchants, that differs from a corresponding second fraud classification defined by a second set of policies associated with a second set of merchants,
• output, via the communication interface, the first fraud classification to a first merchant associated with the first transaction when the first set of merchants includes the first merchant,
• receive, via the communication interface and after outputting the first fraud classification, second transaction data for a second transaction associated with a second consumer that differs from the first consumer,
• identify a plurality of second transaction attributes associated with the second transaction data,
• determine that at least one transaction attribute is common to the first transaction attributes and the second transaction attributes,
• determine a second fraud probability for the first transaction based on the second transaction data, and
• output, via the communication interface, an updated fraud classification to the first merchant based on the second fraud probability as defined by the first set of policies.

A non-transitory computer-readable medium that stores instructions executable by one or more computer devices to perform a method, comprising:

• receiving, via a communication interface, first transaction data for a first transaction associated with a first consumer;
• identifying, by the one or more computer devices, a plurality of first transaction attributes associated with the first transaction data;
• determining, by the one or more computer devices and based on the first transaction data, a first fraud probability for the first transaction;
• determining, by the one or more computer devices, that the first fraud probability corresponds to a first fraud classification, defined by a first set of policies associated with a first set of merchants, that differs from a corresponding second fraud classification defined by a second set of policies associated with a second set of merchants;
• outputting, via the communication interface, the first fraud classification to a first merchant associated with the first transaction when the first set of merchants includes the first merchant;
• receiving, via the communication interface and after outputting the first fraud classification, second transaction data for a second transaction associated with a second consumer that differs from the first consumer;
• identifying, by one or more computer devices, a plurality of second transaction attributes associated with the second transaction data;
• determining, by the one or more computer devices, that at least one transaction attribute is common to the first transaction attributes and the second transaction attributes;
• determining, by the one or more computer devices, a second fraud probability for the first transaction based on the second transaction data;
• and
• outputting, via the communication interface, an updated fraud classification to the first merchant based on the second fraud probability as defined by the first set of policies.