Mastercard Technologies Canada Ulc

John Hearty

Anton Laptiev

Parin Prashant Shah

Igor Opushnyev

Hanhan Wu

Jake Madison

Christopher Bailey

Sik Suen Chan

Randy Lukashuk

Carrie Ka Lai Cheung

Nicholas Desmond

Zhi-Ping Ng

David J Senci

Karamjit Singh

Sangam Verma

Bingjing Yu

Tanmoy Bhowmik

Ashish Kumar

Robert W Capps, Ii

Ryan Brough

Carrie Kai Lai Cheung

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 includes an online application origination (OAO) service. When executing the OAO service, the electronic processor is configured to determine a fraud score of an online application based on an online application origination (OAO) model that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Online application origination (OAO) service for fraud prevention systems

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

A fraud prevention system that includes a fraud prevention server including an electronic processor and a memory. The memory includes an online application origination (OAO) service. When executing the OAO service, the electronic processor is configured to determine whether the OAO service is enabled and whether a website configuration includes a list of multi-page placements for an online application, determine that input data needs to be stored in the memory and combined into multi-page input data, determine a fraud risk score of the online application based on the multi-page input data and an online application origination (OAO) model that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device, and control a client server to approve, hold, or deny the online application based on the fraud risk score that is determined.

Multi-page online application origination (OAO) service for fraud prevention systems

Embodiment described herein provide a system for authenticating an identity of a user. The system includes a first device, a second device, a user device, and a remote computing device. The first device, the second device, and the user device are connected to and operable to communicate over the first communications network. The user device is connected to and operable to communicate over a second communications network. The user device is configured to detect the first device or the second device when the first device or the second device is connected the first communications network, and to generate one or more signals related to the detection of the first device or the second device. The remote computing device is configured to receive the one or more signals from the user device and authenticate the identity of the user based on the one or more signals.

Systems, methods, computer-readable media, and devices for authenticating users

A system for generating a fraud risk score for a third party provider (TPP) transaction. The system includes a server including an electronic processor. The electronic processor is configured to determine a frequency-recency-monetary value feature, a reputation feature, and a rule feature for a TPP transaction, using the frequency-recency-monetary value feature, the reputation feature, and the rule feature as input for a machine learning model, execute the machine learning model to generate a blended score, and, when the blended score is above a second predetermined threshold, determine that the TPP transaction is fraudulent.

Generating a fraud risk score for a third party provider transaction

A fraud prevention server that includes an electronic processor and a memory. The memory includes an online application origination (OAO) service and a plurality of OAO models, each of the plurality of OAO models differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device. When executing the OAO service, the electronic processor is configured to receive form data from a client server, determine a best OAO model from a plurality of OAO models with deep-learning, determine a fraud score of the online application based on the best OAO model, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Deep-learning based system for online application origination (OAO) fraud detection

A system for determining a fraud risk score associated with a transaction. The system includes a server including an electronic processor. The electronic processor is configured to determine a plurality of rules based on a plurality of transactions over time and extract one or more features of the transaction. The electronic processor is also configured to select, based on the plurality of rules, a plurality of fraud risk features Each non-categorical fraud risk feature selected is associated with a fraud risk feature value and each categorical fraud risk feature selected is associated with a categorical variable value. The electronic processor is configured to determine, for each categorical fraud risk feature, a fraud risk feature value. The electronic processor is also configured to determine the fraud risk score based on the one or more of the transformed fraud risk feature values.

Determining a fraud risk score associated with a transaction

A universal device identifier (UDID) service with adjusted attribute distances. In one embodiment, a server includes an electronic processor and a memory storing the UDID service. The electronic processor is configured to receive an identification request regarding a currently-observed device having a first set of device attributes, retrieve any previously-observed devices, determine whether one or more devices are found from retrieving the any of the previously-observed devices, determine all changed device attributes for each device of the one or more devices that are found relative to the first set of device attributes, generate an adjusted attribute distance corresponding to the all changed device attributes for the each device, retrieve a universal device identifier (UDID) of a closest device having a lowest adjusted attribute distance among all adjusted attribute distances that are generated, and assign the UDID of the closest device to the currently-observed device.

Universal device identifier service with adjusted attribute distances

A fraud prevention system that includes a fraud prevention server including an electronic processor and a memory. The memory includes a feature drift hardened online application origination (OAO) service. When executing the feature drift hardened OAO service, the electronic processor is configured to detect, with respect to a first OAO model, feature drift in a dataset of an online application that exceeds a predefined threshold, generate one or more feature drift hardened OAO models that mitigate the feature drift, determine a fraud score of the online application based on the one or more feature drift hardened OAO models that differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device and mitigates the feature drift, and control a client server to approve, hold, or deny the online application based on the fraud score.

Feature drift hardened online application origination (OAO) service for fraud prevention systems

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.

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Vancouver, Canada

John Hearty