The probabilities of transitioning between item states for a given item sequence of a given transaction are calculated and item non-fraud scores are calculated from the probabilities for each item of the given transaction. The item non-fraud scores for the items of the transaction are provided to a fraud-detection system for determining whether any of the item non-fraud scores is more likely or less likely to be associated with sweethearting fraud by a cashier that performed the transaction.

Disclosed is a method and system, in FIG. 1, for verifying authenticity of specific personal data responsive to a unique wallet address (70) on a public ledger (66) of an unregulated platform (60). The wallet contains one or more non-transferable NFTs each locked to the wallet and related to specific personal data. The non-transferable NFT was minted from a regulated platform (27) to which there is a record (76) in the public ledger. Supplying the unique address of the wallet to a service supplier affects a log-in of a client (18) to the service supplier (90), whereafter message interactions over a network (12) between the service supplier and the unregulated platform (60) permits searching for a relevant (72) non-transferable NFTs (74) stored in the wallet and related to the specific personal data. Return of any relevant non-transferable NFT related to the personal data thus verifies authenticity of the specific personal data by association with a regulated platform.

A group of inventions relates to artificial neural networks and their application for computer vision, in particular for the surveillance camera data processing systems and methods to automatically detect cashier fraud by verifying images using artificial neural networks. To detect cashier fraud, a POS system includes a barcode reader, memory, an image capture device, and a data processing module. The data processing module is configured to receive the data about the scanned product from the product database and to receive the video data from the image capture device. An automatic generation of product image set for artificial neural network learning contains stages when the barcode is read by placing an item against a barcode reader by a cashier, the barcode data signal then provides the data about the scanned product from product database, when the barcode data signal gives the image of the placed product from the image capture device, the received image of the placed item is saved with the data about the scanned product in the product database, then the abovementioned stages are repeated for each item placed against the barcode reader.

Supply chain factoring utilizing shared state information stored in a distributed ledger includes the selection of an electronic supply chain document associated with an order for goods by a purchaser of the goods and the minting of a cryptographic token on behalf of a seller of the goods. the token encapsulating a purchase price for the order and associated order terms. A location is reserved in the ledger into which the token is uploaded. Subsequently, factoring terms are published at the reserved location by a factoring agency supporting the factorization of the purchase price. The seller then validates an ascension to the factoring terms in the reserved location. Finally, the reserved location is annotated to indicate satisfaction of the factoring terms upon the purchase price being paid to the factoring agency and a fraction of the purchase price being paid by the factoring agency to the seller of goods.

Embodiments provide an alert management and real-time remediation system. The system receives an electronic file that includes an overdraft allowance information data structure comprising overdraft allowance information and automatically parses the overdraft allowance information into overdraft allowance events for the associated customers. The system sends overdraft allowance notification messages to the associated customers. The message includes a link to an authentication interface of an institution associated with the customer. After the customer is authenticated, the system provides a user interface that displays overdraft allowance information for each overdraft allowance event associated with the customer and a display element for a payment option. Responsive to the customer selecting the payment option, the system sends a payment message that causes a transfer of a payment to the institution.

Disclosed is an approach that applies a fuzzy logic model that may involve fuzzy-matching a plurality of address fields to determine a common physical address, and determining a number of communiques directed to that address with reference to a threshold that may determine an excessive number of communiques. The plurality of address fields may also be fuzzy-matched to information in a fraud-risk database which may comprise a fraud-risk address. One or more matches may be presented to a user who may adjust the views of the various matches, track various trends within the data, and harmonize the various address fields relating to a physical address.

Two models are first trained and then test images are applied to the two trained models in an effort to detect signature forgeries. The first model is trained with pairs of signature images and the resultant trained model is capable of detecting blind forgeries. The second model is trained with triplets of signature images and is capable of detecting skilled signature forgeries. After the two models are trained, test images are applied to the models and determinations are made as to whether a blind or skilled forgery is present.

A real-time fraud prevention system enables merchants and commercial organizations on-line to assess and protect themselves from high-risk users. A centralized database is configured to build and store dossiers of user devices and behaviors collected from subscriber websites in real-time. Real, low-risk users have webpage click navigation behaviors that are assumed to be very different than those of fraudsters. Individual user devices are distinguished from others by hundreds of points of user-device configuration data each independently maintains. A client agent provokes user devices to volunteer configuration data when a user visits respective webpages at independent websites. A collection of comprehensive dossiers of user devices is organized by their identifying information, and used calculating a fraud score in real-time. Each corresponding website is thereby assisted in deciding whether to allow a proposed transaction to be concluded with the particular user and their device.

A detection device for identification and isolation of unauthorized skimmer/shimmer devices takes the form of a portable electronics package adapted for deployment under or near a point-of-sale (POS) station that may be targeted by such skimmer. The detection device is intended for placement near or adjacent an electronic exchange of personal, financial, and/or sensitive information from a payment card, mobile device, or similar magnetic, optical, or radio frequency medium. Unscrupulous interception devices periodically transmit gathered information for reception. The detection device monitors transmissions for those having characteristics indicative of the unscrupulously gathered information, and renders an output signal alerting to the presence and location of an illicit capture device.

A method of reducing a future amount of electronic fraud alerts includes receiving data detailing a financial transaction, inputting the data into a rules-based engine that generates an electronic fraud alert, transmitting the alert to a mobile device of a customer, and receiving from the mobile device customer feedback indicating that the alert was a false positive or otherwise erroneous. The method also includes inputting the data detailing the financial transaction into a machine learning program trained to (i) determine a reason why the false positive was generated, and (ii) then modify the rules-based engine to account for the reason why the false positive was generated, and to no longer generate electronic fraud alerts based upon (a) fact patterns similar to fact patterns of the financial transaction, or (b) data similar to the data detailing the financial transaction, to facilitate reducing an amount of future false positive fraud alerts.