Abstract: Systems, methods, and non-transitory computer readable medium disclosed herein relate to identity verification and authorization method. In one embodiment, the system can generate and send a message to a device associated with a user based on an initiated request from the user and a determination the user should be authenticated, wherein the message requests a content-based response from the user to authenticate the user. In another embodiment, the system can receive the content-based response from the user in reply to the message, wherein the content-based response comprises SMS (short message service) metadata, emoji, photo, video, audio, or a combination thereof. In another embodiment, the system can authenticate the user based on a determination of a confirmed match between the content-based response from the user and a response key preselected by the user.

Abstract: Disclosed herein are systems and methods for account status monitoring. The method can include receiving user registration information and account information corresponding to a first user account. The account information can include first user credit information. The method can include analyzing, by a machine learning rules engine, the account information and the user registration information to create a user behavior pattern associated with the first user account. The method can further include determining an anomaly in the user behavior pattern indicative of a life event. Based on the life event and the user registration information, the method can determine a second user account and corresponding account information comprising second user credit information. In response, the method can include predicting, by a convolutional neural network, an effect on the second user credit information resulting from the transfer of the first user credit information to the second user account.

Abstract: In some implementations, a system may identify, using a machine learning model, a pattern of events based on account information associated with a user. The system may generate a prediction of a time period during which a predicted event, associated with the pattern of events, is predicted to occur. The system may receive an indication that a vehicle, associated with the user, is to travel a route from a starting location to an ending location during the time period. The system may identify a physical location associated with the predicted event based on a location associated with the vehicle or based on the route. The system may generate a new route that includes the starting location, the physical location as a waypoint along the new route, and the ending location. The system may transmit information that identifies the new route to a device associated with the vehicle.

Abstract: Systems and methods of video password based user authentication via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving a request to register a video password from a first user; establishing at least one user-specific authentication criterion for the first user based on first video password data; receiving a login attempt from a second computing device associated with a second user who submits to be the first user, the login attempt comprising second video password data; utilizing a trained video password authentication machine learning model to determine a first comparison result and a second comparison result based on the first and second video password data, and accepting or rejecting the login attempt based on at least one of the first comparison result and the second comparison result.

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 transmit an MLM to one or more entities. The system may receive, from the one or more entities, data associated with a plurality of customers, the data generated by the MLM. The system may train a federated deep NN based on the data. The system may receive, from a user, a first request for a loan. The system may generate, using the federated deep NN, a risk score associated with the user, wherein the risk score comprises a likelihood the user will satisfy condition(s) of the loan. The system may transmit, to lender(s), the risk score and a second request for loan option(s) for backing the first request for the loan. The system may receive, from the first lender(s), the loan option(s) for backing the first request for the loan, and may transmit the loan option(s) to the user.

Abstract: Example embodiments of systems and methods for card production are provided. A card may include processing circuitry including a chip and memory. The card may include one or more antennas in communication with the chip. The card may include a first layer of material aligned within a perimeter of the card via laminate encapsulation. The first layer of material may comprise a non-rectangular shape. The first layer of material may be offset with a shape of the card. The first layer of material may be in communication with the chip. The first layer of material may comprise at least one selected from the group of steel, tungsten, titanium, or any combination thereof. The card may be compliant with one or more form factors.

Abstract: Systems and methods of the present disclosure enable IoT-based social networking to detect, using IoT devices at a location, device signals associated with a first user device of a first user. A first user profile associated with the first user device is determined in response to the device signals. User attributes stored in the first user profile are accessed and extracted. A trained profile similarity model is used to determine similarity measures between the first user profile and other user profiles based on the user attributes. At least one similar user profile to the first user profile is identified based on the similarity measures. A meet-up point occupancy of each meet-up point at the location is determined based on a record of meet-up points to identify an open meet-up point. A meet-up recommendation is generated indicating the open meet-up point and the other user profiles.

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: In some implementations, a system may receive an audio stream associated with a call between a user and an agent. The system may process, by the device and using a speech alteration model, speech from a first channel of the audio stream to alter the speech from having a first speech characteristic to having a second speech characteristic, wherein the speech alteration model is trained based on reference audio data associated with the first speech characteristic and the second speech characteristic and based on reference speech data associated with the first speech characteristic and the second speech characteristic. The system may extract the speech from the first channel that has the first speech characteristic. The system may provide, within a second channel of the audio stream, altered speech that corresponds to the speech and that has the first speech characteristic.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: A system may transmit an MLM to one or more entities. The system may receive, from the one or more entities, data associated with a plurality of customers, the data generated by the MLM. The system may train a federated deep NN based on the data. The system may receive, from a user, a first request for a loan. The system may generate, using the federated deep NN, a risk score associated with the user, wherein the risk score comprises a likelihood the user will satisfy condition(s) of the loan. The system may transmit, to lender(s), the risk score and a second request for loan option(s) for backing the first request for the loan. The system may receive, from the first lender(s), the loan option(s) for backing the first request for the loan, and may transmit the loan option(s) to the user.

Abstract: Logic may identify information of a first electronic mail document associated with a control, the control to comprise a process to mitigate a risk. Logic may select the electronic mail document based on the information. Logic may determine metadata for the electronic mail document, the metadata. Logic may generate a first value representative of the electronic mail document based on application of a mathematical algorithm. Logic may generate a log entry for the electronic mail document comprising the metadata and the first value. Logic may encrypt the log entry with an encryption key. And logic may store the log entry in an encrypted ledger with a log entry date and a unique log entry identifier.

Abstract: In some implementations, a device may determine an interaction profile for a provider that is to engage with a user in a communication session. The interaction profile may be based on interaction data relating to interpersonal interactions involving the provider during one or more previous communication sessions. The interaction profile may indicate a bias of the provider in connection with one or more categories of users. The device may generate, based on the interaction profile, a visual representation that depicts at least a face of a person for presentation to the provider during the communication session. One or more characteristics associated with the one or more categories of users may be absent from the face of the person. The device may cause presentation of the visual representation to the provider during the communication session.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.

Abstract: Systems and methods of video password based user authentication via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving a request to register a video password from a first user; establishing at least one user-specific authentication criterion for the first user based on first video password data; receiving a login attempt from a second computing device associated with a second user who submits to be the first user, the login attempt comprising second video password data; utilizing a trained video password authentication machine learning model to determine a first comparison result and a second comparison result based on the first and second video password data, and accepting or rejecting the login attempt based on at least one of the first comparison result and the second comparison result.

Abstract: Systems and methods for implementing a dynamic cashback adjustment rates based on environmental and social impact derived a user's transactional data are disclosed. The described operations leverage a sustainability scoring model based on user's transactional data to derive three input parameters for quantifying a measures of sustainability factor and environmental impact associated with the user's transactional behavior and purchasing pattern. The computed sustainability score may then be used to generates a ranking based on a user's environmental impact as derived from their transactional activity. The generated ranking is then used to automate cash-back rewards to further incentivize sustainable transactional practices.

Abstract: The disclosed embodiments include a system and method for generating a predictive model and allocating, based on the model's findings, an ideal amount into a user's account. The system includes a user device and a server. The server can retrieve the user's information, analyze the information, generate the predictive model, train the model, calculate a savings amount and allocate the amount to the appropriate account.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from the user to monitor a plurality of activities executed within the computing device; continually monitoring the plurality of activities executed within the computing device for a predetermined period of time; identifying an indication of an incoming interaction session within the predetermined period of time; verifying at least one session interaction parameter associated with the incoming interaction session to identify the incoming interaction session as a repeat interaction session; dynamically retrieving at least one relation-specific notation from a plurality of relation-specific notations to display; instructing an input GUI element to display input data associated with the repeat interaction; automatically updating the plurality of relation-specific notations associated with the historical data relationship; and instructing at least one programmable output GUI to display a notif

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.