The Western Union Company

Noel Brandt

Robert Enzaldo

Charles Champion

Brent Lemieux

Niren Kinikar

Hollis Baugh

Manju Rai

Murali Ravichandran

Jacqueline Molnar

Bryant Gofstein

Hiran Modi

Viren Patel

Alexander Varakin

Alec Hall

Alexander Varaken

Richard Alec Hall

Niren Kinkar

Techniques and systems for determining a malicious derivative entity within a network are provided herein. A method for determining a malicious derivative entity may include receiving, by a network-based authentication system, a plurality of network transactions. A first attribute of a network transaction within the plurality of network transactions may be identified. The method may also include identifying a plurality of entities for the first attribute. The network-based authentication system may generate a first visual representation of a relationship between the first attribute and the plurality of derivative entities. Each of the derivative entities and the first attribute may be represented as nodes within the first visual representation. A first score for each of the nodes may be determined based on a degree of centrality of the nodes within the first visual representation. One network transaction may be blocked based on at least one node exceeding a first threshold.

Detection of a malicious entity within a network

Techniques and systems for detecting malicious activity within a network are provided herein. A method for detecting malicious activity within a network may include receiving, by a network-based authentication system, a network transaction. The network-based authentication system may identify a first attribute of the network transaction. The method may also include selecting, by the network-based authentication system, a first learning statistical model and a second learning statistical model from a plurality of models for handling the network transaction. Each of the first learning statistical model and the second learning statistical model may create a likelihood that the network transaction is authentic. The first learning statistical model may calculate a first score and the second learning statistical score may calculate a second score. Based on a comparison of the first score to a first threshold and the second score to a second threshold, the network transaction may be authenticated.

Detection of malicious activity within a network

Systems and methods for identifying and mitigating non-compliances include determining an agent location of a money service business for review, obtaining a photograph and geographic coordinates of the agent location, and collecting field data at the agent location through a mobile application. Such information are sent from the mobile application to a central server of a third party vendor for a series of quality checks and stored at the central server. Such information may be used for further corrective action against the agent location reviewed.

Methods and systems for real-time identification and mitigation of agent risks relating to AML

A money transfer system includes a risk assessment system and a risk modeling system. The risk assessment system uses rules engines and Z scores to assess, on a real time basis, risk factors associated with money transfers. The risk modeling system develops risk scores based on historical transaction data in order to create a risk model. The risk model may be analyzed over time to refine the rules engines and take other actions to understand and reduce the risk of improper activity in connection with money transfers.

Risk analysis of money transfer transactions

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Highlands Ranch, CO, United States of America

Noel Brandt