Systems and methods for an autonomous marketplace system include a server that extracts borrower data including financial, operational and business data, and lender data from prospective lenders including financial data and a target profile for each prospective lender. It then generates, using artificial intelligence, an autonomous ranked match of prospective lenders. The artificial intelligence identifies relationships between the borrower data and the prospective lender data to generate the autonomous ranked match based on a preference of the borrower and a correlation between the identified relationships. Upon the borrower entering an internet based chat room, the identities of the prospective lenders are revealed. The borrower selects one or more prospective lenders to receive a finance request, and after selection by the borrower, the selected prospective lenders are first notified and informed of an existence of the finance request of the borrower.

A method for creating a customized and redacted credit report may include transmitting a user request to generate a customized credit report, receiving a copy of a stored credit report, analyzing the copy of the stored credit report, displaying one or more information fields and one or more selectable user interface elements, transmitting a credit report redaction list, and receiving a customized credit report. A user device for configuring a customized credit report may include a processor, a display in communication with the processor, and a non-transitory memory storing instructions that, when executed by the processor, cause the processor to perform processing including transmitting a user request to generate a customized credit report, displaying one or more information fields and one or more selectable user interface elements, transmitting a credit report redaction list, and receiving a shareable credit report link associated with a customized credit report.

A method and apparatus for processing risk-management feature factors based on user generated content (UGC), an electronic device and a storage medium are disclosed, which relates to the fields of artificial intelligence and cloud computing. An implementation includes generating a feature expression of the UGC based on the UGC; and extracting the risk-management feature factors of the UGC according to a pre-generated risk-management-feature-factor extracting model and the feature expression of the UGC. According to the technology of the present application, the risk-management feature factors of a corresponding user may be extracted based on the UGC without depending on privacy information of the user, such as personal basic attributes, or the like, such that subsequent related processing actions of risk management may be facilitated, an acquiring way and an acquiring mode of the risk-management feature factors may be enriched effectively, and richer information of the risk-management feature factors may be acquired.

An apparatus includes a memory, a communication interface in communication with a network, a first processor, and a second process different from the first processor. The first processor configured to receive data from a user device and to separate the data into a first data set including metadata associated with a user of the user device and a second data set including anonymized data associated with a set of actions to be performed on a predetermined schedule. The second processor is configured to receive the second data set from the first processor and a user input associated with a selection of a simulation and at least one additional action otherwise not included in the set of actions. The second processor configured to perform the simulation using the second data set to simulate an acceleration of the predetermined schedule as a result of the at least one additional action.

An apparatus includes a memory, a communication interface in communication with a network, a first processor, and a second process different from the first processor. The first processor configured to receive data from a user device and to separate the data into a first data set including metadata associated with a user of the user device and a second data set including anonymized data associated with a set of actions to be performed on a predetermined schedule. The second processor is configured to receive the second data set from the first processor and a user input associated with a selection of a simulation and at least one additional action otherwise not included in the set of actions. The second processor configured to perform the simulation using the second data set to simulate an acceleration of the predetermined schedule as a result of the at least one additional action.

Systems and methods for increasing the training value of input training datasets are described herein. In an embodiment, a server computer receives a plurality of input training datasets, each of the input training datasets comprising values for a plurality of parameters, a value indicating whether failure has occurred, and another value indicating the time of failure or the time of observation if no failure has occurred. For each input training dataset, the server computer generates a plurality of month-specific training datasets, each of which comprising a first value indicating a number of previous months where failure has not occurred and a second value indicating whether failure occurred during a month corresponding to the month-specific training data. The server computer trains a machine learning model using the plurality of month-specific training datasets. When the server computer receives a particular input dataset, the server computer generates a plurality of month-specific input datasets from the particular input dataset and uses the machine learning model to compute a plurality of month-specific likelihoods of failure of the particular item from the plurality of month-specific input datasets. This process allows a machine learning model to train off of both complete and incomplete datasets, giving the machine learning model access to current data and allowing for earlier implementation of machine learning in new business areas.

Examples described herein include systems, methods, instructions, and other implementations for data security with integrated installment payment systems. In one example, account security system receives a checkout communication that includes data describing a validated checkout system of a merchant system. A client token is transmitted in response to an authentication that the checkout communication is from the validated checkout system, and an account communication including the client token and secure client information is received from a client device. An installment payment communication associated with the secure transaction is received from a system other than a merchant system involved in the transaction. The secure transaction is then facilitated following receipt of the installment payment communication.

Examples described herein include systems, methods, instructions, and other implementations for data security with integrated installment payment systems. In one example, account security system receives a checkout communication that includes data describing a validated checkout system of a merchant system. A client token is transmitted in response to an authentication that the checkout communication is from the validated checkout system, and an account communication including the client token and secure client information is received from a client device. An installment payment communication associated with the secure transaction is received from a system other than a merchant system involved in the transaction. The secure transaction is then facilitated following receipt of the installment payment communication.

Intelligent bundling of multiple loans is described. In an example, server computing device(s) can receive a request for a first loan to enable a buyer to purchase a first item from a first merchant and can generate the first loan based at least in part on determining that the buyer purchased the first item. The first loan can be associated with a balance based on an actual purchase price of the first item. The server computing device(s) can determine to bundle repayment of the first loan with at least a second loan, and based on determining to bundle repayment of the first loan and at least the second loan, the server computing device(s) can repay at least a portion of the first loan and at least a portion of the second loan via a single payment from an account of the buyer.

In an illustrative embodiment, an automated system links data files associated with loan submissions that have different identification attributes. The system may include computing systems and devices for receiving requests from a number of remote computing systems to identify loan products associated with a data file. The system can generate a matching input matrix comparing identification attributes from a first data file to identification attributes of candidate data files. The system can apply attribute matching rules to the matching input matrix to identify other data files that correspond to the same loan product as the first data file despite the data files having different identification attributes. The system can link data files corresponding to the same loan product within a data repository with a product linking key and output the linking key or other data for the loan product to a receiving computing system.