Abstract: A method including training a machine-learning model, based on historical data, with a maximization problem and one or more minimization problems to improve one or more fairness metrics. The method also can include receiving real-time data. The method additionally can include generating a risk score based on the machine-learning model, as trained, and the real-time data. Other embodiments are described.

Abstract: A method of deriving a financial conscientiousness score from visual choices, using a machine learning model that is trained at a server is provided. The method includes (i) obtaining visual selection as an input from the user that is selected from one or more different visual choices that are displayed to the user, (ii) determining trait scores for a subset of traits based on the points that are attributed to the visual choices, (iii) training, a machine learning model by correlating the trait scores for the subset of traits with a previous credit history or a loan repayment record of the user to obtain a trained machine learning model, and (iv) determining, using the trained machine learning model, a financial conscientiousness score based on the trait scores for the subset of traits.

Abstract: In some implementations, a credit decision platform may receive a credit request from an applicant and obtain domestic historical data associated with the applicant from a credit bureau device. The credit decision platform may obtain access to an email account associated with the applicant based on determining that the domestic historical data associated with the applicant is insufficient to process the credit request. The credit decision platform may identify, using one or more machine learning models, a set of email messages included in the email account that are relevant to the credit request and may analyze content included in the set of email messages to generate non-domestic historical data associated with the applicant. The credit decision platform may generate a decision on the credit request based on an estimated creditworthiness of the applicant, which may be determined based on the non-domestic historical data.

Abstract: A computerized-method for predicting a probability of fraudulent financial-account access, is provided herein. The computerized-method includes a. building a Machine Learning (ML) sequence model; b. implementing a forward-propagation-routine in an encapsulated environment that runs applications to mimic a process of the ML sequence model. The forward-propagation-routine is mimicking processing of a chronical-sequence of a preconfigured number of non-financial activities sequence vector, layer by layer to generate a fraud probability score and using weights and biases which were extracted from each layer of the trained ML sequence model; and c.

Abstract: Prescreened electronic programs or products that are automatically determined for a specific potential entity based on characteristics and/or geographic location, then can be automatically ranked based on calculated expected values of respective programs or products. The ranked programs or products are digitally/electronically presented in an interactive graphical user interface to the specific entity for digital selection and application.

Abstract: A fraud prevention server that includes an electronic processor and a memory. The memory includes an online application origination (OAO) service and a plurality of OAO models, each of the plurality of OAO models differentiates between a behavior of a normal user and a behavior of a nefarious actor during a submission of the online application on a device. When executing the OAO service, the electronic processor is configured to receive form data from a client server, determine a best OAO model from a plurality of OAO models with deep-learning, determine a fraud score of the online application based on the best OAO model, and control the client server to approve, hold, or deny the online application based on the fraud score that is determined.

Abstract: A system for generating video content may include a bank database that has a first set of data associated with an account status of an individual. The system may also include a credit provider database that includes a second set of data associated with a credit status of the individual and at least one processor. At least one processor may receive an indication that the credit status of the individual has changed, retrieve the first set of data from the bank database and the second set of data from the credit provider database, generate a personalized financial video associated with the individual based on the first set of data and the second set of data, and transmit the personalized financial video to a computing device associated with the individual.

Abstract: To extract information from a customer's insurance card, an insurance policy recordation system obtains a scanned image of an insurance card and analyzes text in the insurance card image to identify labels describing insurance policy characteristics and insurance policy values corresponding to the insurance policy characteristics. The insurance policy recordation system compares the identified insurance policy values to a set of stored identifiers associated with various insurance plans. If the insurance policy values match one or more of the identifiers, the insurance policy recordation system may determine that the insurance policy values are valid and that the customer has the matching insurance plan. The insurance policy recordation system may obtain additional insurance policy information for the customer based on the stored information in association with the matching insurance plan.

Abstract: An embodiment of the present invention is directed to a performance and volume testing for credit card authorization systems. A system that implements performance and volume testing for credit card authorizations comprises: a memory component that stores performance and volume data; a communication interface that supports communication via TCP/IP and a testing tool comprising a processor coupled to the memory component and programmed to: parse data in a message format for use in performance testing; send load volumes to a system under test to gather metrics, the system under test hosts a credit card authorization application; and collect data from production logs in a message format used to replay authorizations against the credit card authorization application.

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: Aspects of the disclosure relate to machine learning based automated management of a user account. A computing platform may determine, via a computing device and based on historical user activity of a user, a pattern of the user activity. Subsequently, the computing platform may detect a deviation from the pattern of the user activity. Then, the computing platform may identify an anticipated transaction of the user. Then, the computing platform may retrieve, from a repository of user data, one or more user-defined preference rules associated with the anticipated transaction. Then, the computing platform may determine whether the one or more preference rules apply to one or more attributes of the anticipated transaction. Subsequently, the computing platform may trigger, based on a determination that the one or more preference rules apply to the one or more attributes of the anticipated transaction, an action associated with the anticipated transaction.

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: Aspects described herein may use a machine learning model to identify transactions likely to be remembered by a user and that may be used to generate challenge questions to authenticate the user. An individual may request an action related to a financial account. In response to the request, the machine learning model may determine a likelihood an authorized user of the financial account will remember one or more recent transactions. The likelihood of each candidate transaction may be compared to a predetermined threshold to determine a subset of recent transactions. Information relating to the subset of recent transactions may be used to generate one or more challenge questions to pose to the user. The user's responses to the challenge questions may be used to evaluate whether the user is the authorized user of the financial account or is a fraudster or imposter.

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: Aspects of the disclosure relate to processing asset transfers. A computing platform may receive an asset transfer request and extract information from it. The computing platform may modify a distributed ledger to include a new block corresponding to the asset transfer request. The computing platform may identify a consensus method by identifying, for each of a plurality of consensus methods, a subset of blocks from the distributed ledger that, when used to execute the corresponding consensus method, result in a lowest energy consumption value in comparison to remaining subsets of blocks. The computing platform may execute each consensus method using the corresponding identified subset of blocks, which may result in establishing consensus to process the asset transfer request. Based on establishing the consensus, the computing platform may direct an event processing platform to process the asset transfer request, which may cause the event processing platform to process the request accordingly.

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: 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: 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.