Abstract: Exemplary embodiments may provide keys for unlocking access to a location, like a user room, a secure location, a door at an employee location, a trunk, a closet or other locked location or item, on a payment card. Examples of a payment card include but are not limited to a credit card, a debit card, a smart card, an employee identification card, etc. A secure token that acts as digital key may be uploaded to the payment card. The payment card may then be put in close proximity of a wireless reader at the lock. The wireless reader obtains the secure token and extracts the contents. If the contents are proper, the lock is unlocked. Otherwise, the lock remains locked.

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: Systems and methods for implementing an automated process for detection of a shared recurring bills and identification of a set of corresponding person-to-person transactions associated with shared portions of the recurring bill. The identification process is prediction-based and contingent upon an analysis of a user's transaction records. A key aspect of the analysis is directed towards transaction amount data (fraction test) and occurrence patterns, derived from the transaction records, in order to identify a shared bill and a set of person-to-person (P2P) transfer transactions associated therewith. Upon identification of a set of P2P transfer transactions associated with a recurrent shared bill, an automation option may be provided to the user.

Abstract: Systems and methods for preventing fraud using behavioral biometrics may include a server with memory and a processor. The processor may be configured to create a behavioral biometric use-print for a user based on a plurality of observed and recorded user interactions and then monitor one or more behavioral biometrics of the user while the user accesses a user account. These monitored behavioral biometrics may be compared against the behavioral biometric use-print to determine if there are material deviations indicating that the user is under stress. When the server determines that the user is under stress, it may provide an intervention to help safeguard against potential in-process fraud.

Abstract: The present embodiments disclose systems and methods for authenticating a user with behavioral biometrics associated with a card. The card includes an accelerometer configured to measure the orientation of the card body. The system includes the card and a software application. The software application generates a user profile based on observed card biometrics, then determines whether a recent transaction significantly deviates from the profile.

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 system and method employs a chatbot into which a user can input an inquiry/question regarding a risk related topic is disclosed. The user can obtain an answer to the inquiry. The system and method can receive, via a chatbot interface, an inquiry. An intent of the inquiry can be determined using a natural language processing (NLP) model. The inquiry can be matched, using a classifier, to a related data set based on the determined intent. An output can be generated based on the related data set in response to the inquiry. The output can be transmitted for display on the chatbot interface.

Abstract: Systems and methods for active medical planning and customized budgeting may include a database that stores medical data, an interface, and a processor. The processor may be configured to receive and parse a medical document for medical procedure and related information, implement a learning algorithm to predict, based on the parsed medical and related information as well as the medical data, one or more medical procedures that the user is likely to undergo subsequent to any medical procedures parsed from the medical document, a cost associated with each predicted medical procedure, and a time horizon for each of the one or more predicted procedures, and train the predictive model with feedback from the user.

Abstract: In some implementations, a flight system may receive structured data including a plurality of event entries. The flight system may detect, within the structured data, at least one of the plurality of event entries that is associated with a flight. The flight system may transmit, to a first application programming interface (API), a call including an identifier associated with the flight as a parameter. Accordingly, the flight system may receive, from the first API, data associated with the flight that indicates a layover. The flight system may estimate a duration and a location associated with the layover and may generate a set of possible meals based on the duration and the location. The flight system may receive an indication of a selected meal from the set of possible meals. Accordingly, the flight system may trigger a call to a second API to confirm the selected meal.

Abstract: In some implementations, a server may receive, from a first user, an indication of a first set of times and receive, from the second user, an indication of a second set of times. The server may determine a first location associated with the first user and a second location associated with the second user. Accordingly, the server may identify a set of events based on the first location, the second location, the first set of times, and the second set of times. The server may further select a subset of events, from the set of events, based on first event data associated with the first user and second event data associated with the second user. Based on a selected event from the subset of events, the server may identify an API associated with the selected event and may trigger a call to the identified API.

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: A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

Abstract: In some implementations, a system may generate a first exchange profile for a first user and a second exchange profile for a second user. The first exchange profile may identify one or more characteristics of an exchange behavior of the first user based on exchange data of the first user, and the second exchange profile may identify one or more characteristics of an exchange behavior of the second user based on exchange data of the second user. The system may receive, from a first device of the first user, a request for a selection for the first user. The system may determine, based on the first exchange profile and the second exchange profile, an entity that is relevant to the first user. The system may transmit, to the first device of the first user, information indicating the selection for the first user, where the selection identifies the entity.

Abstract: In some implementations, a trend prediction system may identify, using a machine learning model, one or more consumers having a historical tendency to adopt one or more trends near a beginning of one or more trend adoption curves. The trend prediction system may predict, using the machine learning model, a consumer trend near a beginning of a trend adoption curve based on a subset of consumer data associated with the one or more consumers having the historical tendency to adopt the trends near the beginning of the trend adoption curves. The trend prediction system may determine, based on the consumer trend that is near the beginning of the trend adoption curve, local trend information in an area associated with a client. The trend prediction system may provide, to a device associated with the client, the local trend information.

Abstract: A server device may be configured to obtain image data that depicts a set of objects associated with a user. The server device may be configured to process, using at least one image analysis technique, the image data to determine identification information for each object of the set of objects. The server device may be configured to obtain exchange data related to at least one exchange log of the user and may be configured to determine, based on the exchange data and the identification information, estimated exchange information for each object of a subset of objects of the set of objects. The server device may be configured to determine, based on the estimated exchange information, estimated assessment information for each object of the subset of objects and may be configured to generate, based on the estimated assessment information, presentation information for display via a graphical user interface (GUI).

Abstract: Exemplary embodiments may provide keys for unlocking access to a location, like a user room, a secure location, a door at an employee location, a trunk, a closet or other locked location or item, on a payment card. Examples of a payment card include but are not limited to a credit card, a debit card, a smart card, an employee identification card, etc. A secure token that acts as digital key may be uploaded to the payment card. The payment card may then be put in close proximity of a wireless reader at the lock. The wireless reader obtains the secure token and extracts the contents. If the contents are proper, the lock is unlocked. Otherwise, the lock remains locked.

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: 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: A system may receive a request to verify an identity of a user. The system may transmit information that identifies a task to be performed via user interaction with a client device and information that identifies behavioral data to be collected by the client device in connection with performance of the task, wherein the task requires at least one of: movement of the client device, interaction with a user interface of the client device using a gesture, or interaction with a user interface of the client device using a cursor. The system may receive the behavioral data and may perform identity verification associated with the user based on the behavioral data and the task. The system may transmit an indication of a recommended action to be performed with respect to the user and the client device based on performing the identity verification.

Abstract: A system may receive, from a server device that provides an application form to a client device, device information associated with the client device, wherein the device information indicates a geolocation associated with the client device. The system may receive, from the server device, behavior information that indicates user behavior associated with inputting data into the application form using the client device, wherein the behavior information indicates a manner in which the data is input into one or more fields of the application form. The system may determine a fraud score based on the device information and the behavior information. The system may transmit an indication of a recommended action to be performed by the server device with respect to the application form and the client device based on the fraud score.