Wells Fargo Bank, N.a.

Beth S Moss

Chris Theodore Kalaboukis

Julio Jiron

Raziq Yaqub

Kevin King

James Lyle Schafer

Kalyan Subramanian

Benjamin W Geertz

Angela C Berkery

Michael H Chang

Ryan S Alexander

Sean S Hamidi

Jason M Stokes

Wendy M Stutz

Ramanathan Ramanathan

Andrew J Garner, Iv

Darrell Lee Suen

Paul Vittimberga

Laura Marie Fontana

Traci H Nguyen

Wayne Barakat

Jeffrey A Cornman

Kevin R Cieslak

Sotirios K Barkas

Christina Zappala Ongpin

Matthew Martinez

Michael J Johnson

Jennifer G Spratley

Fraud detection and response associated with various access points is disclosed. Characteristics associated with events or activity on an access point can be compared to thresholds to detect risky behavior and measures implemented in response to the risky behavior. In one instance, operations can include monitoring one or more events associated with at least one account at a financial institution, analyzing characteristics associated with a first event of the one or more events, determining whether to designate the first event or the access point as risky, and implementing one or more measures in response to a risky designation.

Fraud detection

Systems, methods, and other embodiments associated with a fraudulent activity shell. According to one embodiment, a system includes an application having a normal environment and a shell environment. The system also includes a trigger logic that determines whether an action satisfies a trigger condition. In response to determining that the action satisfies the trigger condition, the trigger logic triggers the application to enter a fraudulent activity mode from a default mode. In the fraudulent activity mode, the normal environment is configured to hold the action. The shell environment is configured to display the action as executed in the fraudulent activity mode.

Fraudulent activity shell

A computer system is described that is configured to enable customers of an organization to self-monitor account activity and modify account access settings via a contact center of the organization. The contact center control system is configured to provide an interactive call history associated with the customer's accounts with the organization. Using the call history, the customer may perform self-monitoring of calls into the contact center that access the customer's accounts, and notify the organization if any of the calls appear suspicious. The contact center control system is configured provide a user interface through which the customer may modify account access settings including changing which types of authentication methods are enabled to access the customer's accounts, and blocking account access by callers from certain phone numbers. The contact center control system may allow the customer to turn off all access to the customer's accounts via the contact center.

Customer self-help control system for contact centers

A call screening computing system is described that is configured to perform voice captcha and real-time monitoring of calls into a contact center of an organization. The call screening computing system includes a chat bot configured to operate as an AI-based call screener. The chat bot is configured to perform voice captcha by sending a random question to a user device placing a call into the contact center, and analyzing the received answer to determine whether a user of the user device is human or a robot. The chat bot is configured to, based on the user being human, determine whether the user is a legitimate customer of the organization by generating and presenting authentication challenges to the user device. The chat bot may be configured to monitor and interact with a conversation between the user and an agent of the organization during the call into the contact center.

Voice captcha and real-time monitoring for contact centers

Automated secondary linking can be employed in conjunction with fraud detection systems. According to one embodiment, a system includes a parsing logic configured to receive an incoming fraud report based on a first fraud detection strategy. The parse logic is also configured to parse the incoming fraud report into fraud data. The system further includes a search logic configured to search a set of fraud reports to identify instances of the fraud data. The system also includes an alert logic configured to generate an alert in response to an instance of the fraud data being identified in the set of fraud reports.

Automated secondary linking for fraud detection systems

Embodiments of the disclosure are directed to authenticating a user at an electronic computing device. Information is received from a defined zone of an environment surrounding the user. A level of authentication is calculated for the user based on the information from the defined zone of the environment. When the level of authentication is greater than or equal to a predetermined threshold, the user is authenticated at the electronic computing device to perform a requested activity.

Defined zone of authentication

A fraud analysis computing system is provided. The system includes a network interface configured to communicate data over a network and a processing circuit including one or more processors coupled to non-transitory memory. The processing circuit is configured to monitor incoming call data generated during an incoming call between a user and an incoming caller, detect a fraud trigger within the incoming call data, and complete a fraud interception activity in response to detection of the fraud trigger.

System and method for intelligent call interception and fraud detecting audio assistant

A computer system is described that is configured to generate an entry in a centralized event log for each voice call into a contact center of an organization. The event log system is configured to receive call data associated with action performed during the call and retrieve context data associated with the call from across a plurality of disparate systems used by the contact center to service the call. The event log system is configured to include both the call data and the context data in the call entry, and to correlate the call entry with previous call entries for a same entity identified for the call. The call entry may also include entity profile data as metadata. The pertinent data for the call will be stored in a single, centralized location accessible by any of the front-end systems for use in determining how to handle the call.

Centralized event log generation and analysis for contact centers

Systems and methods of fraud prevention are discussed. One such method can include the acts of monitoring one or more events associated with at least one account at a financial institution; analyzing characteristics associated with a first event of the one or more events; determining whether to designate the first event as risky; and implementing one or more measures in response to the designation of the first event as risky.

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Danville, CA, United States of America

Beth S Moss