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: 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: The present invention relates to an artificial intelligence method and system for event predication, comprising: receiving, user messages, user activity data, event data, user identification information and transaction data; scraping webpages for additional event data; applying a natural language processing module to process the event data; constructing a training data set using the processed event data; constructing user preferences from the user messages, the user activity data, the user identification information and the transaction data; training a predictive model using the training data set to determine at least one upcoming event predictions determining to display the at least one event predictions based on the user profile; if it is determined to display one of the at least one event predictions, generating a graphical user interface display with a calendar depicting the at least one event prediction; and presenting the graphical user interface display to the user.

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: Methods and systems are disclosed herein for secure communication between computing devices. A mobile device may communicate with an untrusted device to cause the untrusted device to send information (e.g., encrypted information that the untrusted device is unable to decrypt) to a server using an Internet connection of the untrusted device. The mobile device may have limited or no access to the Internet. To prevent potential security risks associated with using a public or untrusted device, the mobile device may encrypt information stored on the mobile device (e.g., stored in a mobile application associated with the server), send it to the untrusted device (e.g., by displaying a QR code to a camera of the untrusted device), and the untrusted device may send the information to the server via a network connection of the untrusted device.

Abstract: Example embodiments of information-shielding cards and systems and methods of fabricating the same are provided. An information-shielding card can comprising a substrate comprising a first layer, a second layer, a third layer, a chip embedded in the second layer, and a quick-response (QR) code formed on the second layer. The second layer can be disposed between the first layer and the third layer. The first layer can comprise a first material that is transparent when exposed to a non-visible light, the second layer can comprise a second material that is opaque when exposed to visible light and when exposed to non-visible light, and the third layer comprises a third material that is transparent when exposed to a non-visible light.

Abstract: Methods and systems are described for improvements to authentication processes. For example, conventional systems may rely on password authentication or contact-based alternative authentication techniques that are impractical or infeasible in sensitive medical environments, during pandemics, or fail meet the health and safety needs of an increasingly health-conscious public. In contrast, the described solutions provide an improvement over conventional authentication systems and methods by enabling user authentication via contactless physiological biometric authentication processes, behavioral authentication processes, and passive data authentication processes, that do not require a user to remove personal protective equipment.

Abstract: A disclosed example includes providing vibration information to a model, the vibration information corresponding to a first vibration measured at a first mobile device when the first mobile device is in a state of non-use by a user, the model based on a plurality of vibration patterns that correspond to second vibrations measured by second mobile devices in different environments; identifying, using the model, one of the vibration patterns that corresponds to the vibration information; determining an environment of the first mobile device based on the one of the vibration patterns; and instructing the first mobile device to modify a functionality of the first mobile device based on the environment.

Abstract: Techniques are disclosed to use existing vehicle speakers alone or in conjunction with other sensors (e.g. SRS sensors and/or microphones) that may already be implemented as part of the vehicle to identify acoustic signatures. Suitable low-cost and widely available hardware components (e.g., relays) may be used to modify the vehicle's existing speakers for a bi-directional mode of operation. Moreover, the vehicle's existing of audio amplifiers may be used to amplify signals collected by the speakers when operating in “reverse,” and process these collected signals to determine vehicle state information.

Abstract: Systems and methods of the present disclosure include computer systems for improving data discovery and recommendation. To do so, activity records associated with multiple entities are received, including activity data for electronic activities with the entities. An entity classification model engine including an entity classification model is utilized to predict at least one entity-type classification classifying at least one first entity of the entities as a first entity type. A first plurality of entity-related activity characteristics representing an activity pattern is associated with the at least one first entity and is extracted from the activity data. An entity rating model engine comprising an entity rating model is utilized to predict at least one entity rating prediction for the at least one entity based at least in part on the activity pattern, and an entity rating interface is generated comprising the at least one entity rating prediction interface element.

Abstract: Systems as described herein may analyze big data to generate predictions for Software-as-a-Service (SaaS) offerings and/or other subscription-based software. A prediction system may receive small business transaction data comprising payment information between a plurality of merchants and a software vendor. The prediction system may receive enterprise merchant insights information. The prediction system may identify, based on the small business transaction data and the enterprise merchant insights information, recurring transactions using a first machine learning model. The prediction system may generate an average contract price and a confidence score, using a second machine learning model. The prediction system may generate a recommendation comprising a renewal rate. Based on a determination that a service provided by the software vendor is coming due for renewal, the prediction system may send, to a particular merchant, an alert associated with the renewal and the recommendation.

Abstract: Example embodiments of systems, methods, and computer-accessible mediums for transaction authorization are provided. An exemplary system can comprise a card including an input device and a display device in data communication with a server. The server can generate an authorization passcode upon an initiation of a transaction session, and the card can receive an entered passcode through the input device, display the entered passcode on the display device, and transmit the entered passcode to the server for comparison to the authorization passcode. Upon a determination that the entered passcode is a match for the authorization passcode, the server can transmit a match notification indicating the transaction session is valid, and upon a determination that the entered passcode is a mismatch for the authorization passcode, the server can transmit a mismatch notification terminating the transaction session.

Abstract: A system for dynamically routing customer calls. For example, the system may receive user interaction data associated with a first user using a first user device. The system may also receive a phone call from a user using a first phone number. The system may also identify the user via the first phone number. The system may determine, using a first machine learning model, whether the first user has a first emotion type based on the user interaction data. When the first user does not have the first emotion type, the system may route the first user to any call center representative. When the first user has the first emotion type, the system may route the first user to a first call center representative among one or more first call center representatives.

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: Example embodiments of systems, methods, and computer-accessible mediums for transaction authorization are provided. An exemplary system can comprise a card including an input device and a display device in data communication with a server. The server can generate an authorization passcode upon an initiation of a transaction session, and the card can receive an entered passcode through the input device, display the entered passcode on the display device, and transmit the entered passcode to the server for comparison to the authorization passcode. Upon a determination that the entered passcode is a match for the authorization passcode, the server can transmit a match notification indicating the transaction session is valid, and upon a determination that the entered passcode is a mismatch for the authorization passcode, the server can transmit a mismatch notification terminating the transaction session.

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: Methods and apparatuses for implementing a neural network using symmetric tensors. In embodiments, a system may include a higher order neural network with a plurality of layers that includes an input layer, one or more hidden layers, and an output layer. Each of the input layer, the one or more hidden layers, and the output layer includes a plurality of neurons, where the plurality of neurons includes at least first order neurons and second order neurons, and where inputs at a second order neuron are combined using a symmetric tensor.

Abstract: In some implementations, a transaction card may include a card body having a first surface and a second surface opposite the first surface. The card body may include a clip element that is defined by a slit in the card body from the first surface to the second surface. The clip element may be configured to flex relative to the card body. The transaction card may include at least one of an integrated circuit chip or a magnetic strip adjoined to the card body.

Abstract: Systems, apparatuses and methods may provide for origination camera technology that extracts data from multimodal sensor signals associated with a scene and generates a five-dimensional (5D) representation of the scene, wherein the 5D representation includes a three-dimensional (3D) visual representation, a one-dimensional (1D) temporal representation, and a 1D branch representation. The technology may also store the 5D representation as a set of abstract descriptors.