Abstract: System and method for electronic order management processing may include a sequencer that updates a parent order status maintained at the sequencer based on an order instruction and generates and multicasts to interface processors of a host server a sequence of events generated based on order instructions and a current order state of the parent order. An event may indicate an order instruction of or related to the parent order, confirmation of completion of a client order or an exchange order of a trade of a portion of the parent order, or an order instruction related to the parent order determined to be in conflict with the current order state. Events may be stored on a queue at a host server according to global sequence numbers assigned at the sequencer, and the interface processors may determine a next action for order processing based on the events on the queue.

Abstract: Systems and methods for activity risk management are disclosed. A system for activity risk management may include a memory storing instructions and at least one processor configured to execute instructions to perform operations including: accessing document data associated with at least one of a transaction or an individual; normalizing the document data; classifying the normalized document data; extracting model input data from the classified document data; applying a machine learning model to the extracted model input data to score the document data, the machine learning model having been trained to generate a favorability output indicating a favorability of the transaction or individual; and generating analysis data based on the scored document data.

Abstract: An apparatus and computerized method for automated loan origination processing via a computer system with communications devices for receiving loan application data and user provided information. Logic mechanisms programmed to automatically generate a loan origination compliance task workflow having an organized sequence of a plurality of compliance tasks in response to the loan application data received from a user. Receiving at a central computer server a loan application having information provided by the user via the user computer terminal and acquiring at the central computer server electronic data relating to the user from one or more databases responsive to receiving the user loan application information. Initiate a review process for the loan application utilizing the user provided information and data acquired from the one or more databases and render an approval decision for the electronic loan application based upon the review process.

Abstract: A self-modifying data container for improved data security and methods of use are disclosed. The self-modifying data container includes a data storage structure for storing financial transaction information. The self-modifying data container also includes a data manager stored as code within the container. The data manager can run on a system hosting the self-modifying data container. The data manager can access transaction information in the data storage structure and modify the data in response to modification triggers. The data manager can delete data in the data storage structure. The data manager can also encrypt data in the data storage structure. The self-modifying data container improves both data security and data privacy.

Abstract: An apparatus for producing an autonomy score is disclosed. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a pecuniary datum. The memory additionally instructs the processor to generate a pecuniary plan as a function of the pecuniary datum. The memory then instructs the processor to evaluate a pecuniary proficiency of a user as a function of the pecuniary plan. A pecuniary machine learning model is configured to be trained using a pecuniary training data. The pecuniary proficiency of a user is then evaluated as a function of the pecuniary plan. The memory then instructs the processor to produce an autonomy score as a function of the pecuniary proficiency. The memory finally instructs the processor to determine a pecuniary status of a user as a function of the pecuniary proficiency and the autonomy score.

Abstract: A non-transitory computer-readable storage medium storing computer-readable program code executable by a processor to retrieve a customer data record including bill payment activity associated with a provider account for a customer of a financial institution; and define a geographical footprint for the customer. The geographical footprint may be defined by identifying a customer set including the customer from a plurality of customers of a provider of the provider account, having customer records indicating geographic proximity, and determining that a predetermined fraction of the customer set are associated with a common billing cycle for the provider. The computer-readable program code may be executable by the processor to send a bill payment alert for transmission to the customer via electronic communication or telephonic communication, when the bill payment activity meets a threshold condition, wherein the threshold condition is determined at least in part by the geographical footprint.

Abstract: In some implementations, a device may receive an indication of an event that is associated with a first account and an entity, and an amount associated with the event. The device may establish a record, in a relational database, to link an identifier associated with the first account with the event. The device may receive, from a device associated with the first account, a request to associate the event with the first account and a second account. The device may modify the record to link the identifier associated with the event with the identifier associated with the first account and an identifier associated with the second account. The device may update a first balance associated with the first account to reduce the first balance and update a second balance associated with the second account to increase the second balance based on modifying the record.

Abstract: A computer-implemented method for predicting interchange charges includes: retrieving a historical transactions set, where each completed transaction in the set includes transaction features, a bank identification number (BIN), and a corresponding true interchange code; transforming all BINs in the set into a corresponding plurality of BIN features that comprise probabilities; creating a first training set including all transaction features, all pluralities of BIN features, and all true interchange codes associated with the historical transactions set; training a random forest model using the first training set and generating a second training set including rounded BIN features, rounded transaction features, discrete ones of the transaction features, and the true interchange codes; training the random forest model using the second training set to generate a trained random forest model for prediction of the interchange codes; and executing the trained random forest model for new transactions to generate corres

Abstract: An example operation may include one or more of identifying a data record to be included in a blockchain, creating a group of smart contracts to enable access to the data record to data consumers with access to the blockchain, determining the group of smart contracts have been registered by one or more blockchain peers, and storing the group of smart contracts on the blockchain.

Abstract: In one embodiment, a method includes storing, in a data store maintained by a payment service, transaction history information associated with two financial accounts of a user. Upon detecting an intent by the user to transfer funds between the first financial account and the second financial account via an application executing on a device of the user, a prediction of a suggested amount of funds for a user to transfer from the first financial account to the second financial account is generated. The payment service sends instructions to the user's mobile device to display the suggested amount of funds with a request to confirm the transfer. Upon receiving confirmation from the mobile device, the payment service initiates the transfer.

Abstract: A system is described that identifies, based on contextual information associated with a device that is located at a physical location associated with a merchant, a product that a user of the device is at the physical location to purchase. The system executes an autonomous search query for product information that is predicted to assist the user in completing a purchase of the product, from the merchant, at the physical location. The system sends the product information to the device, and for subsequent output. The system determines whether a degree of likelihood that the user will complete the purchase in response to receiving the product information satisfies a likelihood threshold, if not, the system executes a remote assistance module accessed by the device to provide a virtual environment in which a human provides additional information that the user needs to complete the purchase.

Abstract: A method and system provide estimated tax refund data to a user of a tax return preparation system throughout personalized tax return preparation interview. The method and system receive current user tax related data associated with the user, retrieve tax rules data, and gather historical tax related data associated with historical users of the tax return preparation system. The method and system further generate probabilistic inference data including inferences about tax related characteristics of the user based on the historical tax related data and the tax rules data. The method and system provide estimated tax refund data to the user based on the probabilistic inference data.

Abstract: Disclosed are systems and techniques for dynamically providing real-time offers. For instance, user eligibility for one or more offers can be determined based on a plurality of dynamic user attributes associated with the user and one or more data sets corresponding to similarly situated users. An input can be received corresponding to an acceptance by the user of at least one offer selected from the one or more offers. In response to receiving the input, an updated plurality of the dynamic user attributes can be retrieved, wherein the dynamic user attributes are constantly updated in real time based on user activity. Based on the updated plurality of dynamic user attributes and the one or more data sets corresponding to the similarly situated user, it can be determined whether the user remains eligible for the at least one offer. An offer confirmation can be sent upon confirming eligibility.

Abstract: A system and method of operating a digital financial system to extend access to digital banking to those individuals that never had a financial or digital currency account. Users are able to share their internet with other users of the digital banking app of this invention, who are nearby and have no internet connection, to conduct financial transactions by sharing the internet between them. This increases social and financial inclusion to allow unbanked that have a smartphone to have access to the internet and to affordable digital banking. It also improves on the prior art with legal compliance of country of residence verification aided by technology rather than relying solely on information provided by the users. Thereby requesting stricter verification documents to such very small minority of users who are flagged as potentially non-compliant and leave the vast majority alone enjoying their digital banking experience without exhaustive or annoying processes.

Abstract: Systems and methods are disclosed for brand and license hierarchy management to improve brand governance. A hierarchal data structure permits tracking of products, associating items separately with brands and licenses, so that a subset of items associated with a particular brand may have differing license associations. The hierarchal structure advantageously facilitates nesting of primary and secondary (or more) brands and licenses to provide for more reliable automated royalty payment management that can reduce risks of missing payments or making unnecessary duplicate payments. A factory tracking functionality assists compliance with regulatory and ethical sourcing requirements to help reduce reputational and financial risks, for example with private brands, exclusive brands, and imported items.

Abstract: Various methods, apparatuses/systems, and media for implementing a natural language order fill module are disclosed. A database stores data corresponding to trade details of a particular trade. A processor is operatively coupled to the database via a communication network. The processor configures a free-form text box to receive user input data corresponding to the trade details (e.g., abbreviations data, varying sentence syntax data, special characters data and details data, etc.) of the particular trade; implements neural networks algorithm to interpret and extract key trade details data from the receive user input data corresponding to the trade details of the particular trade regardless of text structure of the input data; and automatically fills in corresponding trading ticket fields in real-time based on the extracted key trade details data.

Abstract: Systems and methods for generating tree-based models with improved fairness are disclosed. The disclosed process generates a first tree-based machine learning model, which is preferably trained to predict if a financial loan will be repaid. The process also determines an accuracy of the first tree-based machine learning mode. In addition, the process determines a fairness of the first tree-based machine learning model. The fairness is preferably associated with at least one of gender, race, ethnicity, age, marital status, military status, sexual orientation, and disability status. The process then generates a second different tree-based machine learning model, which is preferably trained based on the accuracy of the first tree-based machine learning model and the fairness of the first tree-based machine learning model. The process then combines the first tree-based machine learning model and the second tree-based machine learning model to produce a gradient-boosted machine learning model.

Abstract: A payment facilitating and processing system includes a payment terminal. The payment terminal includes a processor and a first receiver. The first receiver is configured to communicate with the processor. The payment facilitating and processing system further includes a second receiver, which is also configured to communicate with the processor. The payment facilitating and processing system still further includes a mobility assistance device including a transmitter. The transmitter is configured to receive payment information from a payment apparatus and transmit the payment information to the second receiver upon positioning of the transmitter in close proximity to the second receiver.

Abstract: The system and methods provide an account holder with a mobile spend feed. The systems and methods include a database that stores information associated with at least one previous financial transaction performed via a financial account held by the account holder, a transaction processor that receives transaction data associated with a financial transaction performed by the account holder using the financial account, and stores the transaction data in the database, and a purchase history processor creates a spend feed based at least in part on the transaction data and the previous transaction information, and provides the spend feed to the account holder.

Abstract: A method for providing smart auto rental estimatics to a user includes training a machine learning model to predict repair information, receiving a report, analyzing the report to identify a coverage, determining a vendor, receiving a chosen vendor, determining a branch, receiving a chosen branch, analyzing the report and a volume data using the trained machine learning model to determine loss information, calculating out-of-pockets, and transmitting the out-of-pockets to the user.