Explaining the decisions of AI modules to a user is difficult.

The invention relates to methods for providing an explanation dataset (2) for an AI module (31), the methods comprising: receiving a user dataset (20) which specifies at least one input dataset (21) of an AI module (31), wherein the AI module (31) is adapted to compute an output dataset (3) for the input dataset (21), wherein the user dataset (20) comprises at least one target specification (25) which specifies a value of a data item (26) in an output dataset (3) of the AI module (31); loading at least one optimization task (16) which specifies a specific metric (14) and/or a similarity metric (15); computing at least one solution of the at least one optimization task (16) as an explanation dataset (2) taking the user dataset (20) and the AI module (31) into consideration and applying at least one optimization method (17), wherein the AI module (31) is adapted to compute for the explanation dataset (2) an output dataset (3) which comprises the data item (26) specified by the target specification (25); providing the explanation dataset (2) for the AI module (31).

Embodiments of the present disclosure may include a method for credit underwriting, the method including receiving a dataset of user details. Embodiments may also include creating a convolutional neural network (CNN) with the dataset of user details. In some embodiments, the convolutional neural network organizes at least a portion of the dataset of user details into a layered and weighted dataset. Embodiments may also include creating an enriched layered and weighted dataset. Embodiments may also include executing a back-propagation operation to remove at least a portion of the layered and weighted data from the layered and weighted dataset. Embodiments may also include receiving a plurality of enriched layered and weighted datasets.

Methods and apparatuses for performing prediction based on merchant transaction data are described. In some embodiments, a method comprises: receiving, by one or more payment processing systems, from a plurality of merchant computing devices associated with a plurality of merchants, respectively, transaction data of transactions performed between the plurality of merchants and a plurality of customers; applying, by the one or more payment processing systems, an interpretable machine learning model framework to produce predictions based at least on the transaction data; determining whether to provide loan financing to merchants based on the predictions; and providing, by the payment processing system, loan financing to an account of at least one merchant in response to the determination, including configuring repayment terms individually for each of the merchants receiving the loan financing.

Methods and apparatuses for performing prediction based on merchant transaction data are described. In some embodiments, a method comprises: receiving, by one or more payment processing systems, from a plurality of merchant computing devices associated with a plurality of merchants, respectively, transaction data of transactions performed between the plurality of merchants and a plurality of customers; applying, by the one or more payment processing systems, an interpretable machine learning model framework to produce predictions based at least on the transaction data; determining whether to provide loan financing to merchants based on the predictions; and providing, by the payment processing system, loan financing to an account of at least one merchant in response to the determination, including configuring repayment terms individually for each of the merchants receiving the loan financing.

Computing devices, methods, systems, and computer-readable media for analyzing requests to perform cold restores of cryptocurrencies are described herein. A computing device may receive a request for a cold restore of one or more cryptocurrencies stored by a digital wallet. The one or more cryptocurrencies may be identified, and data may be received from a database. The computing device may determine, based on the data, a risk score associated with the transfer of the one or more cryptocurrencies from a cold state to a hot state. The risk score may be generated using a machine learning model, such as a machine learning model that may be trained to output a risk score associated with a cold restore based on recent transaction activity. The computing device may output, based on comparing the risk score to a threshold, an indication of whether the request should be granted.

Computing devices, methods, systems, and computer-readable media for analyzing requests to perform cold restores of cryptocurrencies are described herein. A computing device may receive a request for a cold restore of one or more cryptocurrencies stored by a digital wallet. The one or more cryptocurrencies may be identified, and data may be received from a database. The computing device may determine, based on the data, a risk score associated with the transfer of the one or more cryptocurrencies from a cold state to a hot state. The risk score may be generated using a machine learning model, such as a machine learning model that may be trained to output a risk score associated with a cold restore based on recent transaction activity. The computing device may output, based on comparing the risk score to a threshold, an indication of whether the request should be granted.

A contract analysis and generation method and system is provided. The method comprises a sequential flow of one or more events resulting in contract analysis and single-click contract generation. The system comprises a plurality of machine learning, natural language processing, and artificial intelligence based modules located on one or more devices or servers, wherein users may interact with the system via user devices.

A contract analysis and generation method and system is provided. The method comprises a sequential flow of one or more events resulting in contract analysis and single-click contract generation. The system comprises a plurality of machine learning, natural language processing, and artificial intelligence based modules located on one or more devices or servers, wherein users may interact with the system via user devices.

A system comprises a processor configured to receive user criteria associated with an operation and to obtain a plurality of segment candidates from a plurality of sources, each source configured to provide a segment of the operation that corresponds to a respective segment candidate obtained from said source. The processor is configured to determine a plurality of operation structure candidates for the operation. Each operation structure candidate is determined based on aggregating a respective subset of the plurality of segment candidates. The processor is configured to determine a priority for each operation structure candidate based on prioritization rules stored in memory of the system and based on the user criteria. The processor is configured to communicate an output indicating one or more of the operation structure candidates for the operation. The output is based on the priority.

A system comprises a processor configured to receive user criteria associated with an operation and to obtain a plurality of segment candidates from a plurality of sources, each source configured to provide a segment of the operation that corresponds to a respective segment candidate obtained from said source. The processor is configured to determine a plurality of operation structure candidates for the operation. Each operation structure candidate is determined based on aggregating a respective subset of the plurality of segment candidates. The processor is configured to determine a priority for each operation structure candidate based on prioritization rules stored in memory of the system and based on the user criteria. The processor is configured to communicate an output indicating one or more of the operation structure candidates for the operation. The output is based on the priority.