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.

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.

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.

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.

A sales processing apparatus includes a processor and an input and output unit configured to perform an operation for a sales transaction processing. The processor is configured to acquire the time required for the input and output unit to perform the operation for the sales transaction processing of a sales transaction, then determine whether the time required for the input and output unit to perform the operation during the sales transaction exceeds a reference time for the input and output unit to perform the operation. A notification is output if the time required for the input and output unit to perform the operation during the sales transaction exceeds the reference time.

The invention provided herein generally relates to devices, systems, and methods for handling cash or quasi-cash items in such a way as to substantially eliminate employee theft, error, or difficulties in reconciling a record of transactions with a total amount of money in a cash drawer. The invention also provides devices, systems, and methods for cash-collateralized electronic banking.

A system for anomaly detection in a self-checkout environment, comprising a processing unit for receiving transaction data from a self-checkout terminal: characterising an activity based on a set of features extracted from the received transaction data; defining a plurality of active intervals for each characterised activity; determining a meta-feature vector for each defined active interval of the plurality of active intervals; comparing each meta feature vector with a predefined set of vectors; and determining an anomaly based on the comparison.

An interactive shopping system, or smart cart. The system allows a user to select items in a store and complete a purchase without having to wait in lines or interact with store personnel. The system can have a container for holding products selected by at least one user, a user interface, a scanner in communication with the user interface, a processor in communication with the user interface, and a data storage comprising computer instructions executable by the processor. A user can select products, place them within the container, scan the products, and initiate a payment transaction to complete a purchase.

A method includes supporting, by a support, a bag containing one or more items of an order and detecting, by a sensor coupled to an end of the support, a weight of the bag and the one or more items when the bag is suspended on the support such that the weight of the bag and the one or more items is exerted on the support. The method also includes comparing, by a hardware processor, the detected weight to an expected weight of the order and communicating, by the hardware processor, an alert when the expected weight is unequal to the detected weight.

In some embodiments, apparatuses and methods are provided herein useful to self-checkout verification at a retail facility. In some embodiments, there is provided a system for self-checkout verification at a retail facility including a first optical imaging unit; and a control circuit. The control circuit configured to: receive purchase receipt data; receive one or more images of the items in the container; and execute a machine learning model trained to: perform item detection, item classification, and item verification of each item shown in the one or more images; and output electronic data corresponding to an electronic receipt of the items in the container. The control circuit may automatically detect each unpaid item in the container based on a comparison of the purchase receipt data with the electronic data; and provide an alert signal in response to automatically detecting an unpaid item.