Abstract: Systems, apparatuses, and methods are provided herein for processing a merchandise return transaction. A system for processing a merchandise return transaction comprising: a user interface for allowing direct entry of information by a customer without assistance from a worker; a database; and a control circuit coupled to the user interface and the database, the control circuit configured to execute the return transaction by: obtaining from the customer identifier relating to a purchase transaction in which at least one item was previously purchased; identifying a product item from the purchase transaction to be returned; obtaining from the customer a reason for the product item to be returned; obtaining from the customer a preferred tender for the return transaction; authorizing the return; and providing instructions to the customer for returning the product item to a retail facility.

Abstract: Provided is a method that includes determining cuisine type classifiers based on merchant data, receiving transaction data associated with each payment transaction of a plurality of payment transactions involving a plurality of merchants and a plurality of consumers; determining, with at least one processor, a plurality of cuisine type labels based on a plurality of merchant names associated with the plurality of payment transactions or a portion of the plurality of payment transactions, wherein a cuisine type label identifies a cuisine type provided by a respective merchant of the plurality of merchants; generating a merchant cuisine type model from the transaction data to determine a cuisine type label of a merchant, the merchant cuisine type model based on the transaction data, and makes an inference based on a combination of merchant or consumer features associated with a payment transaction of the plurality of payment transactions; and automatically predicting one or more cuisine type classifications usi

Abstract: A computerized-method for real-time detection of financial transactions suspicious for money-laundering, by processing high-speed streaming financial data. In a computerized-system receiving a financial data stream comprised of data points. Operating a Fused-Density (FD)-based clustering module that is configured to: (i) read the data points; (ii) maintain a grid system; (iii) maintain one or more provisional clusters (PROC)s; (iv) associate each data point with a grid or merge it to a PROC; (v) systemize the grid system and the PROCs; (vi) trim one or more grids and remove one or more PROCs; (vii) form one or more shape devise clusters based on the PROCs; and (viii) transmit the one or more shape devise clusters for analysis thereof, thus, enabling detection of financial transactions suspicious for money-laundering according to the one or more shape devise clusters which were formed out of the high-speed streaming financial data with money-laundering changing trends.

Abstract: Systems and methods that may be used to provide a predictive tax loan or other monetary advance before the loan recipient (e.g., a taxpayer) prepares and files its tax return. A risk of providing a predictive tax loan or monetary advance is modeled separately from a machine learning model used to determine the anticipated tax refund amount and tax loan. The disclosed systems and methods may also predict accurate tax withholdings based on multiple machine learning models from multiple services, including non-payroll related services.

Abstract: A facility for representing home attribute values for a plurality of homes and a plurality of home attributes is described. The facility incorporates a data structure comprising, for each of the plurality of homes, for each of the plurality of attribute value sources, the capacity to store values for any of the plurality of home attributes obtained from the attribute value source.

Abstract: The present disclosure involves systems, software, and computer implemented methods for transaction auditing. One example method includes determining valid pixel-based pattern(s) that are included in valid reference images. Fraudulent pixel-based pattern(s) that are included in fraudulent reference images are determined. A request to classify an image is received. A determination is made as to whether pixel values in the image match a valid pixel-based pattern or a fraudulent pixel-based pattern. In response to determining that the pixel values match a valid pixel-based pattern, a likelihood of classifying the first image as a valid image is increased. In response to determining that the pixel values match a fraudulent pixel-based pattern, a likelihood that the image as a fraudulent image is increased. The image is classified in response to the request as either a valid image or a fraudulent image based on the likelihoods.

Abstract: A method of integrating electronic data including integrating a desktop such that the desktop includes information received from various sources. The information displayed may be analyzed by a business rule to prioritize what information is included in the desktop, and the information may include data from databases, recent member activities, planned future actions, and recommended future actions. The method also includes the ability to integrate and/or aggregate various data sources, such as databases. The combination of the data sources may be performed according to one or more business rules.

Abstract: Described herein is a relationship based fulfillment system. Such a system may provide on-demand product fulfillment by personal and/or social contacts of a purchaser. The system may utilize the personal contacts and/or relationships of a user, as indicated through various contact databases or social groups of the user, to connect the user to various potential partners for providing relationship based fulfillment of products that are purchased by the user or performance of other relationship based services. Furthermore, the system described herein may allow for the utilization of one or more currencies, such as cryptocurrencies, within the system. Techniques are described herein to allow for the utilization of such currencies in a resource efficient manner.

Abstract: A system for providing notifications to client devices that includes one or more processors and one or more storage devices. The storage devices may store instructions that, when executed, configure the processors to perform operations. The operations include receiving a transaction notification from a third party; identifying a user account associated with the transaction notification; generating a push notification with a payload describing instructions to display a message and an interactive icon, and a resource identifier associated with the interactive icon, where the resource identifier includes a message ID encoding an interactive session and an action ID encoding a requested action. The operations also include transmitting the push notification to a client device associated with the user account; receiving a first indication indicating a user interaction with the interactive icon, the first indication having the resource identifier and updating the user account based on the action request.

Abstract: An embodiment method for managing geographic information includes receiving, at a service portal, executed lease data for mineral rights leases on associated first land parcels, generating first map data according to the executed lease data, and sending the first map data through a network interface to a client application for display to a first user at a first remote terminal. The method further includes receiving, from the first remote terminal, data for a proposed division order boundary, determining one or more of the first land parcels having boundaries that at least partly overlap the proposed division order boundary, generating division order data related to portions of the one or more first land parcels, and sending the division order data to the first remote terminal for display to the user.

Abstract: Aspects of the disclosure relate to a message processing platform for enhanced phishing message detection. A computing platform may receive a first message from a first source entity, where the first message was flagged as potentially malicious through selection of a malicious message button in a mobile banking application, and where the first message includes content from the first source entity pretending to be a second source entity different than the first source entity. Using one or more machine learning algorithms, the computing platform may parse the first message to identify the second source entity. The computing platform may identify an enterprise security system associated with the second source entity, and may generate a second message that includes the first message as an attachment and indicates that the first message was flagged as potentially malicious. Subsequently, the computing platform may send, to the enterprise security system, the second message.

Abstract: A payment system, comprising: a receipt apparatus; a payment apparatus, the payment apparatus being connected to the receipt apparatus by means of electromagnetic induction, such that the receipt apparatus can receive information about the payment apparatus; and an identity recognition apparatus, the identity recognition apparatus being disposed in a payment environment and separated from the payment apparatus spatially, and the identity recognition apparatus being in signal connection with the receipt apparatus, wherein when a payer pays money, the identity recognition apparatus is used for recognising identity information about the payer, wherein the payment environment is a place where the receipt apparatus and the payment apparatus are located simultaneously. The payment system ensures the safety, convenience and efficiency of payment. Also provided are a payment device and a payment method.

Abstract: Disclosed herein is a method where a consumer employs a device associated with the user to scans tags associated with items for purchase. The consumer can pay for the items using the device associated with the user. A point of sale (“POS”) terminal or other such device associated with the merchant scans tags associated with items associated with the user and reconciles the items scanned by the device associated with the user with the items scanned by the device associated with the merchant.

Abstract: A system and method classify insurance documents. The system and method receive a request to access documents from an assessment queue stored in a memory of a device where each of the documents contain insurance data and have an associated destination and associated metadata. The system and method convert the set of documents encoded in a first file format to a second file format to remove the metadata. The system and method partition each document of the set of documents into separate stand-alone documents and converts each of the partitioned documents into separate recognition vectors. The system and method classifies the partitioned documents through an additive learning algorithm in which routing data is embedded in second metadata and merge the classified partitioned documents. The merged documents are routed to a remote destination independent of the originally intended destination and the associated metadata.

Abstract: Methods and systems for automatically discovering data types required by a computer-based rule engine for evaluating a transaction request are presented. Multiple potential paths for evaluating the transaction request according to the rule engine are determined. An abstract syntax tree may be generated based on the rule engine to determine the multiple potential paths. Based on an initial set of data extracted from the transaction request, one or more potential paths that are determined to be irrelevant to evaluating the transaction request are identified. Types of data required to evaluate the transaction request according to the remaining potential paths are determined. Only data that corresponds to the determined types of data is retrieved to evaluate the transaction request.

Abstract: A device for processing electronic payments for the purchase of goods or services is provided. The device includes one or more computer processors in communication with non-transitory computer readable data storage and a display. The data storage includes instructions stored thereon that, when executed by the one or more processors, cause the device to execute a transaction process including receiving purchase data representing one or more goods or services to be purchased, receiving user input to effect a payment transaction, determining a total transaction amount from the purchase data, reading payment credentials from a purchaser's payment device, sending a payment request, receiving data representing a received one time password (OTP), sending the received OTP to the third party authorization server for authentication against a OTP sent by the third party authorization server to a mobile device, and receiving data representing successful authentication from the third party authorization server.

Abstract: This disclosure describes techniques for determining whether to approve or deny a borrower's lending-product request by selectively using a heuristic and statistical model. More specifically, a borrower may submit a lending-product request to a Heuristic-Statistical Risk Management (HS-RM) system, and in doing so the HS-RM system may analyze relationship attributes of the borrower to determine a likelihood of borrower repaying a loan over a predetermined time period, and avoid being charged off. In some examples, the HS-RM system may execute a plurality of statistical models to determine a charge-off probability score. Each statistical model may be based on a set, or subset of historical lending-product data. A subset of historical lending-product data may be based on a selection bias of shared characteristics within a set of historical lending-product data. The selection bias may be based on characteristics of a lending-product request or relationship attributes of a borrower.

Abstract: Methods and systems for using machine learning to automatically determine a data loading configuration for a computer-based rule engine are presented. The computer-based rule engine is configured to use rules to evaluate incoming transaction requests. Data of various data types may be required by the rule engine when evaluating the incoming transaction requests. The data loading configuration specifies pre-loading data associated with at least a first data type and lazy-loading data associated with at least a second data type. Statistical data such as use rates and loading times associated with the various data types may be supplied to a machine learning module to determine a particular loading configuration for the various data types. The computer-based rule engine then loads data according to the data loading configuration when evaluating a subsequent transaction request.

Abstract: Methods, systems, and apparatuses are described herein for the direct sharing and use of transaction data separately from transaction authorization processes. Transaction metadata associated with a transaction may be received and validated. Authorization information corresponding to the transaction may be received. The degree to which the transaction metadata is tested may be based on a predicted time of receipt of the authorization information. The transaction metadata and authorization information may be correlated. A computing device may determine whether to authorize the financial transaction based on the authorization information and the correlated transaction metadata. All or portions of the transaction metadata may be provided to one or more users after the transaction has been authorized or denied.

Abstract: The disclosed embodiments relate to systems and methods for matching orders for a financial product. A first order for a financial product is received. The first order includes a preference for a first order book. An order processer selects a first hardware match processor based on the preference. A first hardware match processor attempts to match the first order with a previously received order in the first order book. A second order for the financial product is received. The second order includes a preference for a second order book. The order processor selects a second hardware match processor based on the preference. A second hardware match processor attempts to match the second order with a previously received order in the second order book. The first and second hardware match processors use different matching algorithms.