Mastercard Technologies Canada Ulc

Jake Madison

John Hearty

Parin Prashant Shah

Christopher Bailey

Randy Lukashuk

Anton Laptiev

Igor Opushnyev

Hanhan Wu

Sik Suen Chan

Nicholas Desmond

Zhi-Ping Ng

Bingjing Yu

A fraud prevention system that includes a client server and a fraud prevention server. The fraud prevention server includes an electronic processor and a memory. The memory including a trust scoring service. When executing the trust scoring service, the electronic processor is configured to receive a trust score request of a device from the client server, generate, with a trust model, a trust score of the device, and responsive to generating the trust score, output the trust score to the client server in satisfaction of the trust score request, wherein the trust score is distinct from a risk factor, the trust score representing a predicted trust level of the device, and the risk factor representing a fraud risk level associated with the device based on one or more device behaviors.

Trust scoring service for fraud prevention systems

An anomaly detection system that includes a database and a server. The server is connected to the database. The server is configured to identify anomalous web traffic for a certain time period based on one or more client keys from the certain time period. The client key(s) includes at least two characteristics related to web traffic data. The server includes a processing unit and a memory. The server is configured to receive the web traffic data from the database, calculate a z-score metric for the client key, calculate a change rate metric for the client key, calculate a failure metric for the client key, determine an anomaly score based on the z-score metric, the change rate metric, and the failure metric, and determine that the certain time period is an anomalous time period based on the anomaly score.

Automated web traffic anomaly detection

An account recommendation system that includes a server. The server is operable to identify a user account based on a server-side persistent device identification. The server includes a processor and a memory. The server is configured to receive a request including one or more attributes related to a client device, identify the server-side persistent device identification ('PDI') record for the client device based on the one or more attributes of the client device, identify one or more candidate accounts based on the server-side persistent device identification, determine confidence scores for each of the one or more candidate accounts, generate a recommendation signal for the user account associated with the client device based on the confidence scores, and transmit the recommendation signal to a merchant server.

Account recommendation based on server-side, persistent device identification

A fraud prevention system that includes a server. The server is operable to receive a first attribute of a client device from the client device and associated with a first transaction, receive a second attribute of the client device from the client device and associated with the first transaction, receive a third attribute related to the client device and associated with the first transaction, and generate a persistent device identification ('PDI') record including the first attribute, the second attribute, and the third attribute, store the PDI record in a memory, receive the third attribute related to the client device and associated with a second transaction, and identify the client device using the PDI record based on the third attribute without receiving, in association with the second transaction, the first attribute of the client device and the second attribute of the client device.

Server-side persistent device identification for fraud prevention systems

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Vancouver, Canada

Jake Madison