Abstract: Payment card slot tampering at a transaction terminal is detected in real time. One or more images from one or more cameras are analyzed. The camera(s) is/are focused overhead of the terminal on an area adjacent to and in front of a card slot for a card reader of the terminal. The images are analyzed for determining whether hands of arms for an operator of the terminal are performing actions that indicate the operator may be attempting to install a card skimmer/shimmer into the card slot and/or attempting to remove an existing skimmer/shimmer from the card slot. When card slot tampering event is detected at the terminal a variety of automated actions are processed, such as shutting down the card reader, shutting down the terminal, confiscating any payment card inserted into the card slot, notifying a financial institution, and/or notifying legal authorities.

Abstract: Visual features of a vehicle, a vehicle's orientation with respect to a transaction terminal, and an individual associated with the vehicle are derived from video captured of the terminal and a surrounding area of the terminal. Additional information associated with brute force attacks in a location associated with the terminal are obtained as additional features. The visual features and the additional features are provided as input to a trained Machine-Learning Model (MLM) and the MLM provides as output a confidence value representing a prediction as to whether the current vehicle and the current individual are potentially about to engage in a brute force attack on the terminal. When the confidence value exceeds a threshold value, enhanced feature detection is enabled on the video, external systems are notified, and an incident packet of information is assembled.

Abstract: Visual features of a vehicle, a vehicle's orientation with respect to a transaction terminal, and an individual associated with the vehicle are derived from video captured of the terminal and a surrounding area of the terminal. Additional information associated with brute force attacks in a location associated with the terminal are obtained as additional features. The visual features and the additional features are provided as input to a trained Machine-Learning Model (MLM) and the MLM provides as output a confidence value representing a prediction as to whether the current vehicle and the current individual are potentially about to engage in a brute force attack on the terminal. When the confidence value exceeds a threshold value, enhanced feature detection is enabled on the video, external systems are notified, and an incident packet of information is assembled.

Abstract: Payment card slot tampering at a transaction terminal is detected in real time. One or more images from one or more cameras are analyzed. The camera(s) is/are focused overhead of the terminal on an area adjacent to and in front of a card slot for a card reader of the terminal. The images are analyzed for determining whether hands of arms for an operator of the terminal are performing actions that indicate the operator may be attempting to install a card skimmer/shimmer into the card slot and/or attempting to remove an existing skimmer/shimmer from the card slot. When card slot tampering event is detected at the terminal a variety of automated actions are processed, such as shutting down the card reader, shutting down the terminal, confiscating any payment card inserted into the card slot, notifying a financial institution, and/or notifying legal authorities.

Abstract: An item return transaction for an item is identified at a transaction terminal. The item is authenticated using multiple factors at least some of which are independent of item code identification for the item. A fraud score is calculated based on the multiple factors, item code identification, and data that is specific to a customer associated with the transaction, specific to the item, specific to a store associated with the transaction terminal, and specific to a retailer associated with the store. The fraud score and a customer-return grade for the customer are processed to determine whether the transaction can complete at the terminal without assistance or whether the transaction is to be held in abeyance for an audit (onsite audit or remote network-based audit). In an embodiment, the t terminal is a Self-Service Terminal (SST) and the transaction is a self-item return transaction conducted by a customer at the terminal.

Abstract: A managed device associated with an error code is identified. An animation associated with physically manipulating a component and parts of the component for the managed device is generated. The animation representing a workflow for resolving the error code. A live video feed of the managed device and the physical surroundings of the managed device is presented within an Augmented Reality (AR) interface on a mobile device operated by a support person. The animation is rendered over a portion of the live video feed for the support person to view while following the workflow and physically manipulating the component and parts of the managed device to resolve the error code.

Abstract: Visual features of a vehicle, a vehicle's orientation with respect to a transaction terminal, and an individual associated with the vehicle are derived from video captured of the terminal and a surrounding area of the terminal. Additional information associated with brute force attacks in a location associated with the terminal are obtained as additional features. The visual features and the additional features are provided as input to a trained Machine-Learning Model (MLM) and the MLM provides as output a confidence value representing a prediction as to whether the current vehicle and the current individual are potentially about to engage in a brute force attack on the terminal. When the confidence value exceeds a threshold value, enhanced feature detection is enabled on the video, external systems are notified, and an incident packet of information is assembled.

Abstract: Cash trapping at an Automated Teller Machine (ATM) is detected in real time. One or more images from one or more cameras are analyzed. The camera(s) is/are focused overhead of the ATM or on a cash slot of a dispense module for the ATM. The images are analyzed for determining one or more of whether the dispenser module is authentic, whether the cash slot opened or did not open when it should have opened for a cash withdraw, whether visual features of the dispense module have changed over a configurable period of time, and whether hands, gestures, and actions of a person present at the ATM indicate that cash trapping is taking place at the ATM. When cash trapping is detected at the ATM a variety of automated actions are processed, such as shutting down the dispense module, shutting down the ATM, notifying a financial institution, and/or notifying legal authorities.

Abstract: Payment card slot tampering at a transaction terminal is detected in real time. One or more images from one or more cameras are analyzed. The camera(s) is/are focused overhead of the terminal on an area adjacent to and in front of a card slot for a card reader of the terminal. The images are analyzed for determining whether hands of arms for an operator of the terminal are performing actions that indicate the operator may be attempting to install a card skimmer/shimmer into the card slot and/or attempting to remove an existing skimmer/shimmer from the card slot. When card slot tampering event is detected at the terminal a variety of automated actions are processed, such as shutting down the card reader, shutting down the terminal, confiscating any payment card inserted into the card slot, notifying a financial institution, and/or notifying legal authorities.

Abstract: Cash trapping at an Automated Teller Machine (ATM) is detected in real time. One or more images from one or more cameras are analyzed. The camera(s) is/are focused overhead of the ATM or on a cash slot of a dispense module for the ATM. The images are analyzed for determining one or more of whether the dispenser module is authentic, whether the cash slot opened or did not open when it should have opened for a cash withdraw, whether visual features of the dispense module have changed over a configurable period of time, and whether hands, gestures, and actions of a person present at the ATM indicate that cash trapping is taking place at the ATM. When cash trapping is detected at the ATM a variety of automated actions are processed, such as shutting down the dispense module, shutting down the ATM, notifying a financial institution, and/or notifying legal authorities.

Abstract: A managed device associated with an error code is identified. An animation associated with physically manipulating a component and parts of the component for the managed device is generated. The animation representing a workflow for resolving the error code. A live video feed of the managed device and the physical surroundings of the managed device is presented within an Augmented Reality (AR) interface on a mobile device operated by a support person. The animation is rendered over a portion of the live video feed for the support person to view while following the workflow and physically manipulating the component and parts of the managed device to resolve the error code.

Abstract: A mapping of a physical environment and objects within the physical environment are generated. The objects are tracked within the physical environment and any transaction data associated with transaction correlated with the objects. The objects, relationships between each object, and any transaction data are evaluated to assign a risk score or an assessment needs score. Each score is mapped to one or more unique visual features and a heat map is generated that depicts the objects within the physical environment along with their visual features. The heat map is continuously updated in real time as scores change for the objects and the visual features change. The heat map is rendered within an interface of a device operated or worn by an attendant to assist the attendant in quickly ascertaining any risk of loss, any risk of threat, and/or any customer in need of assistance within the physical environment.

Abstract: Real-time video is captured of a pickup area for orders at a store. The images are analyzed and tracked for unique orders being placed in the pickup area and orders being removed from the pickup area. A customer-operated device is operated by a customer to identify the store where the customer placed an order in a remote location from the pickup area. Images of the orders that are present within the pickup area and order identifying information for the orders are provided to the customer via the customer-operated device.

Abstract: An item return for an item is identified and a workflow is determined based on first factors defined in customized rules. Resources associated with the workflow may be staged on devices associated with the item return and the workflow is initiated. When the item is detected as being present at a drop-off location for the item, item and customer information are gathered in accordance with the customized rules associated with the workflow. Security checks are processed using second factors defined in the customized rules. A decision is made based on the security checks as to whether the item return can continue processing with the workflow or as to whether an item return exception is to be raised for evaluation and/or inspection of the item and item return information collected before the item return exception was raised.

Abstract: An item return for an item is identified and a workflow is determined based on first factors defined in customized rules. Resources associated with the workflow may be staged on devices associated with the item return and the workflow is initiated. When the item is detected as being present at a drop-off location for the item, item and customer information are gathered in accordance with the customized rules associated with the workflow. Security checks are processed using second factors defined in the customized rules. A decision is made based on the security checks as to whether the item return can continue processing with the workflow or as to whether an item return exception is to be raised for evaluation and/or inspection of the item and item return information collected before the item return exception was raised.

Abstract: A real-time cloud-based Augmented Reality (AR) service is provided for events. The service visually and audibly integrates remote participants into event-based locations and event-based devices during an event via participant-operated devices. The service further permits remote ordering of items and/or services by the participants. The items and/or services are actual items and/or services which are being offered at an event venue for the event to in-person event participants. Further, the service renders images of any ordered items/services on AR generated objects of the participant-operated devices. Moreover, the service places the orders with the appropriate vendors and schedules delivery of the ordered items/services to coincide with participant-defined times before, during, and/or after the event.

Abstract: An item return transaction for an item is identified at a transaction terminal. The item is authenticated using multiple factors at least some of which are independent of item code identification for the item. A fraud score is calculated based on the multiple factors, item code identification, and data that is specific to a customer associated with the transaction, specific to the item, specific to a store associated with the transaction terminal, and specific to a retailer associated with the store. The fraud score and a customer-return grade for the customer are processed to determine whether the transaction can complete at the terminal without assistance or whether the transaction is to be held in abeyance for an audit (onsite audit or remote network-based audit). In an embodiment, the t terminal is a Self-Service Terminal (SST) and the transaction is a self-item return transaction conducted by a customer at the terminal.

Abstract: An item return for an item is identified and a workflow is determined based on first factors defined in customized rules. Resources associated with the workflow may be staged on devices associated with the item return and the workflow is initiated. When the item is detected as being present at a drop-off location for the item, item and customer information are gathered in accordance with the customized rules associated with the workflow. Security checks are processed using second factors defined in the customized rules. A decision is made based on the security checks as to whether the item return can continue processing with the workflow or as to whether an item return exception is to be raised for evaluation and/or inspection of the item and item return information collected before the item return exception was raised.