Abstract: Methods and systems disclosed herein describe customizing searching. Search queries may be customized according to a user's preferences. A user may emphasize or indicate that additional weight should be given to one or more terms in a search query. Terms that are weighted higher may have a larger impact on the results that are returned in response to the search query. In addition to changing the terms in a search query, a user may provide a weight for each term. Each term in a search query may be weighted to varying degrees, giving a user more control over the results that are returned. The weights may be used with machine learning techniques to generate a vector representation of a search query. The vector representation of the search query may be compared with vector representations of search objects to determine results that match the search query.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: In some implementations, a storage optimization system may receive a plurality of emails. Accordingly, the system may identify at least one email associated with a limited capacity in the plurality of emails. The system may further scan, from the at least one email, one or more hyperlinks to determine a website associated with the at least one email and an identifier associated with an event. The system may determine, using a database, a traversal path and at least one application programming interface (API) call associated with the website. Accordingly, the system may traverse the website using the traversal path and the at least one API using the identifier to determine that the limited capacity is filled. The system may delete the at least one email associated with the limited capacity based on determining that the limited capacity is filled.

Abstract: Various embodiments are directed to a system or platform with machine learning capabilities configured to accurately predict in real-time a depreciation factor of a vehicle associated with a customer and further accurately predict a present value of the vehicle based at least in part on card transaction data associated with the customer. Based on one or more factors, such as a determination that the present value of the vehicle falls below a predefined threshold value, one or more auto financing products may be generated and provided to the customer by the system.

Abstract: Systems, apparatuses and methods may provide for technology that adjusts a plurality of weights in a neural network model and adjusts a plurality of activation functions in the neural network model. The technology may also output the neural network model in response to one or more conditions being satisfied by the plurality of weights and the plurality of activation functions. In one example, two or more of the activation functions are different from one another and the activation functions are adjusted on a per neuron basis.

Abstract: Aspects described herein may allow for detecting fraudulent transaction requests using light sensors on smartcards. A computing device may receive, from a terminal, a request to approve a transaction. The request may comprise a timestamp corresponding to a time when the transaction was requested, a transaction type associated with the transaction, and an identification of a card associated with the transaction. The computing device may also receive, from one or more light sensors associated with the card, light data. The light data indicates an amount of ambient light during a time period when the transaction was requested. The computing device may determine, based on the transaction type, a model of expected ambient light. If the light data corresponds to the model, the computing device may send, based on the determination that the light data corresponds to the model, an approval of the request.

Abstract: Example embodiments of systems, methods, and computer-accessible mediums for identity verification and transaction authentication are provided. An exemplary system can comprise an application, a user device, and a server. The application can prompt a removal of a card chip, prompt an insertion of the card chip into the user device, determine an orientation of the card chip after the insertion of the card chip into the user device, and transmit, to the card chip, a first message. The card chip can encrypt the first message via one or more authentication protocols to generate an encrypted first message, transmit, to the server, the encrypted first message. The server can decrypt the encrypted first message, verify the decrypted first message, and transmit a second message to the application, wherein the application is configured to display a verification notification in response to the second message.

Abstract: A system may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.

Abstract: Commercial emails are rewritten to aid user understanding and usability. A commercial email is identified from a set of email messages received by an email client for a user. The content of the commercial email is analyzed to identify salient facts associated with the terms of a sale specified by the content. A simplified subject line for the commercial email is generated based on the salient facts and used to replace the original subject line. Priority scores can also be computed based on user transaction history, time, or other factors. The priority scores can then be utilized to organize commercial emails. Further, commercial emails can be segmented from other emails, such as personal or work emails.

Abstract: Systems and methods of the present disclosure enable improved content filtering by determining a profile state of the user profile based at least in part on at least one attribute of the user profile and determining a user profile criterion associated with the user profile based on the profile state including a threshold value to trigger a filter parameter associated with the profile objective, where the filter parameter comprises a content type to be processed so as to facilitate achieving the profile objective. The filter parameter is determined based on the profile state surpassing the user profile criterion. A content recognition model is used to identify a message content of each message. An electronic message that matches the content type is identified and filtered at the electronic messaging client to prevent the content from being presented.

Abstract: In some implementations, a storage optimization system may receive a plurality of emails. Accordingly, the system may identify at least one email associated with a limited capacity in the plurality of emails. The system may further scan, from the at least one email, one or more hyperlinks to determine a website associated with the at least one email and an identifier associated with an event. The system may determine, using a database, a traversal path and at least one application programming interface (API) call associated with the website. Accordingly, the system may traverse the website using the traversal path and the at least one API using the identifier to determine that the limited capacity is filled. The system may delete the at least one email associated with the limited capacity based on determining that the limited capacity is filled.

Abstract: A method and system include identifying, by a processor, departing flight information that designates departure airports and departure times in payment card transaction data of a plurality of users. Airport-specific data for a departure airport before a departure time of a departing flight of a user from the plurality of users is received. The airport-specific data is inputted into a machine learning model that outputs a user-specific airport processing time for the user to reach a departure gate upon arriving to the departure airport. A travel time from a geographical location of the computing device of the user to the departure airport is received from a navigation system. The computing device displays a time for the user to start travel to the departure airport based on the user-specific airport processing time and the travel time to the departure airport for the user to reach the departure gate by the departure time.

Abstract: Aspects described herein may allow for detecting fraudulent transaction requests using light sensors on smartcards. A computing device may receive, from a terminal, a request to approve a transaction. The request may comprise a timestamp corresponding to a time when the transaction was requested, a transaction type associated with the transaction, and an identification of a card associated with the transaction. The computing device may also receive, from one or more light sensors associated with the card, light data. The light data indicates an amount of ambient light during a time period when the transaction was requested. The computing device may determine, based on the transaction type, a model of expected ambient light. If the light data corresponds to the model, the computing device may send, based on the determination that the light data corresponds to the model, an approval of the request.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving an input text data retrieved from a transcription associated with a previously recorded audio data file between a user of a plurality of users and an agent associated with a call center; identifying personal information associated with the user of the plurality of users from the input text data by inputting the input text data into a trained machine learning model; determining at least one key term within the personal information associated with the user of the plurality of users; automatically determining a confidence positivity score associated with the at least one key term; automatically extracting a plurality of tuples from the input text data; storing the plurality of tuples in an external database; and automatically generating a call script for conducting a subsequent call with the user.

Abstract: A system may receive transaction information associated with a transaction, the transaction information comprising an account number associated with a first user. The system may determine whether the transaction is associated with a second user. Responsive to determining the transaction is associated with the second user, the system may transmit a first prompt to a first user device associated with the first user, the first prompt comprising a request to confirm or deny the transaction. The system may receive, via the first user device, a first user selection confirming the transaction. Responsive to receiving the first user selection, the system may determine whether the transaction exceeds a predetermined threshold. Responsive to determining the transaction exceeds the predetermined threshold, the system may transmit a second prompt to the first user device, the second prompt comprising a recommendation to designate the second user as an authorized user of the account number.

Abstract: Embodiments discussed herein are generally directed to systems, devices, methods, and techniques to perform authentication and payment operations with a contactless card to provide items and services.

Abstract: Embodiments are directed to systems and techniques to generate random numbers via contactless cards.

Abstract: Disclosed embodiments may include a system that may receive an interaction message associated with an interaction a user has with an application or website, the interaction message may include an error message or a repeated action message. The system may identify, using a first machine learning model, one or more issues associated with the interaction message, retrieve one or more troubleshooting steps mapped to the one or more issues, and generate a first message comprising the one or more troubleshooting steps and a feedback request on an effectiveness of the one or more troubleshooting steps. The system may transmit the first message to the user, receive feedback from the user in response to the feedback request, and determine whether the feedback is negative. When the feedback is negative, the system may transmit a second message to a representative requesting the representative call the user.

Abstract: A processor may receive data and generate a quantified representation of the data by processing the data using at least one machine learning (ML) algorithm, the quantified representation of the data indicating a sentiment of content of the data. The processor may automatically revise the content of the communications data. The revising may include determining a reaction to the content of the communications data, generating a quantified representation of the reaction, determining a difference between the quantified representation of the reaction and the quantified representation of the communications data, identifying, based on the difference, a portion of the content having an unintended sentiment, and replacing the portion of the content with different content.

Abstract: Disclosed embodiments may include a system that may receive an indication that a user is accessing an ATM, receive, from the ATM, average session duration data over a predetermined period, generate, using a machine learning model, a busyness score for the ATM based on the average session duration data over the predetermined period, and determine whether the busyness score for the ATM exceeds a busyness score threshold. When the busyness score for the ATM does not exceed the busyness score threshold, the system may cause the ATM to present, via a first graphical user interface, a default ATM experience. When the busyness score for the ATM exceeds the busyness score threshold, the system may cause the ATM to present via, a second graphical user interface, a busy ATM experience.