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: Security video is captured when one or more individuals are detected as entering a secure area. Actions of the one or more individuals are monitored based on security policies associated with the secure area. Identifiers for the one or more individuals and action identifiers for the actions are recorded in a security log while the one or more individuals remain in the secure area. The security video is stopped when the individuals are detected as exiting the secure area. The security video is linked to entries recorded in the security log. In an embodiment, when a non-compliant action is detected from the actions based on the security policies, a real-time alert is raised, and/or the security video is streamed in real time to a security monitor.

Abstract: An identity of a customer within an establishment is authenticated using a variety of captured biometric features obtained from sensors and/or video. Video capturing movements/interactions of the customer is analyzed in real time to identify the customer's behavior and actions. Any staff of the establishment who interact with the customer are identified from the video. Transaction data and other data retained for the customer by the establishment are aggregated and linked with the video and the customer identity. The linked data is analyzed in combination with the customer behavior and actions to determine responses within the establishment to customer-initiated transactions. In an embodiment, the customer is authorized to perform at least one transaction within the establishment based on the authenticated identity and linked data without a presentation by the customer of an identification card, a Personal Identification Number (PIN), a password and/or verification by a staff member.

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: Security video is captured when one or more individuals are detected as entering a secure area. Actions of the one or more individuals are monitored based on security policies associated with the secure area. Identifiers for the one or more individuals and action identifiers for the actions are recorded in a security log while the one or more individuals remain in the secure area. The security video is stopped when the individuals are detected as exiting the secure area. The security video is linked to entries recorded in the security log. In an embodiment, when a non-compliant action is detected from the actions based on the security policies, a real-time alert is raised, and/or the security video is streamed in real time to a security monitor.

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: 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: An identity of a customer within an establishment is authenticated using a variety of captured biometric features obtained from sensors and/or video. Video capturing movements/interactions of the customer is analyzed in real time to identify the customer's behavior and actions. Any staff of the establishment who interact with the customer are identified from the video. Transaction data and other data retained for the customer by the establishment are aggregated and linked with the video and the customer identity. The linked data is analyzed in combination with the customer behavior and actions to determine responses within the establishment to customer-initiated transactions. In an embodiment, the customer is authorized to perform at least one transaction within the establishment based on the authenticated identity and linked data without a presentation by the customer of an identification card, a Personal Identification Number (PIN), a password and/or verification by a staff member.

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: Individuals, business transactions, and business activities are monitored for actions and behaviors of the individuals during performance of establishment processes through video feeds captured by cameras, sensor data captured by sensors, and information captured by transaction systems. Transaction/activity information associated with transactions/activities being processed by a transaction system of the establishment or performed by individuals within the establishment are monitored. The actions, behaviors, transaction information, activity information, and establishment processes are correlated to process controls, policies, and procedures of the establishment and logged. Non-compliant actions, behaviors, transaction information, activity information, and/or transaction/activity thresholds generate real-time remedial actions, such as audits, training, and notifications.

Abstract: Zones a defined within an enterprise. Video captured from the zones are monitored for threats (financial or physical). Any transaction data associated with transactions are monitored with the video along with sensor data captured from sensors within the zones. Threat policies are evaluated to identify actions and behaviors of individuals within the zones and threat scores are maintained. When a current threat score associated with a specific type of threat for a given individual or set of individuals exceeds a threshold, one or more automated remediation or threat avoidance actions are processed to mitigate and/or prevent the perceived threat.

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: 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: 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 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: Multiple cameras capture videos within a secure room. When individuals are detected as entering the room, identities of the individuals are resolved. When an asset is exposed in a field of view of one of the cameras, the individuals' eye and head movements are tracked from the videos with respect to one another and the asset. Additionally, touches made by any of the individuals on the asset are tracked from the videos. The eye and head movements are correlated with the touches or lack of touches according to a security policy for the asset. Any violations of the security policy are written to a secure audit log for the room and the asset.

Abstract: An asset is identified in a predefined area. The type of asset is identified through video or wireless tag identification. A security policy associated with the type of asset is obtained. Entries in an asset log are recorded based on a location of the asset, an individual handling the asset, and/or actions taken by an individual with respect to the asset based on the security policy. A real-time notification is sent when the location, the handling, and/or any of the actions warrant a notification based on the security policy.