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: 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: 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: 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: In some implementations, a device may receive information indicating a set of parameters for a partition of a decentralized autonomous organization (DAO) implemented by a distributed database. The set of parameters may include a first parameter indicating a condition for completion of the partition and a second parameter indicating a digital asset wallet of a recipient that is to receive digital assets associated with the partition upon completion of the partition. The device may cause a smart contract, associated with the partition, to be added to the distributed database. The smart contract may include executable code that codifies the set of parameters. The smart contract may be configured to cause, responsive to satisfaction of the condition for completion of the partition, a record to be added to an additional distributed database indicating an association between a digital asset wallet of the partition and the digital asset wallet of the recipient.

Abstract: Systems and methods of anti-vishing OTP protection via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving a permission indicator identifying a permission by the user to detect OTPs and calls being received by a computing device; receiving an indication of an OTP data item being received; processing the OTP data item to determine a time duration during which a particular OTP included therein is valid; utilizing a trained OTP protection machine learning model to determine phone number(s) as presenting a security risk with respect to the OTP data item; and instructing the computing device to commence at least one security measure based at least in part on a contact list updated with an indication that the phone number(s) present a security risk with regard to the particular OTP during the time duration of the particular OTP.

Abstract: Systems and methods of caller identity verification and notification are disclosed. In one embodiment, an exemplary computer-implemented method may include: configuring a notification application, executing at a first computing device associated with a first user, the first user associated with an entity; receiving, from the notification application executing on the first computing device, an indication when the first user initiates a particular call to a second user; determining an application associated with the entity and installed on the second computing device of the second user, the application having an icon displayed at a screen of the second computing device; and instructing the application, executing on the second computing device, when the second computing device has received the particular call from the first user, to update an appearance of the icon associated with the application to display an indicator with regard to that the first user is associated with the entity.

Abstract: In some implementations, a device may obtain interaction data for an evaluator in a communication session with an individual based on interactions of the evaluator with the individual during the communication session, wherein the evaluator is to provide the evaluation of the individual. The device may obtain evaluation information identifying the evaluation of the individual by the evaluator. The device may determine a degree of bias of the evaluator toward the individual during the communication session based on at least the interaction data. The device may adjust the evaluation of the individual based on the degree of bias of the evaluator.

Abstract: Systems and methods of the present disclosure enable database search. The systems and/or methods may include receiving a search query that includes an input document having text. Word embeddings are generated within the input document, where the word embeddings include vector representations of words in the text of the input document. An average input document word embedding vector is determined for the word embeddings of the input document. A set of stored documents is accessed, where each stored document includes a stored text has a particular average stored document word embedding vector. A similarity model is used to determine a similarity metric measuring the similarity between the input document and each stored document based on the average input document word embedding vector and the particular average stored document word embedding vector of each stored document.

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: Disclosed herein are system, method, and computer program product embodiments for providing secure access to a locker of a locker system based on a token that includes token data and a message authentication code (MAC) to authenticate the token data. The token data includes at least the unique locker identifier for the locker. The MAC can be generated using a secret key. The secret key can be obtained based on a unique derivation key associated with a master key. The locker system can determine whether the token is valid based on the token data, the MAC, and the secret key. In response to a determination that the token is valid, the locker system can send a signal to unlock the locker.

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: 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: 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: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a trained spam upsurge detection machine learning model that determines when a current frequency associated with spam communications received by a current user exceeds a baseline frequency associated with the current user; receiving a permission indicator identifying a permission by the user to detect communications being received by the computing device; receiving an indication of at least one communication being received; determining the at least one communication as a particular spam communication; updating a frequency at which spam communications have been received by the user based at least in part on the particular spam communication; utilizing the trained spam upsurge detection machine learning model to determine that the frequency exceeds a baseline frequency associated with the user; and initiating a scan of one or more dark web resources.

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; automatically verifying a session interaction parameter associated with the incoming interaction session; determining a plurality of inquires associated with the research interaction session based on the at least one session interaction parameter; identifying the plurality of demographic informational items associated with the user; and automatically updating the programmable GUI element to display the plurality of inquires associated with the interaction session and the plurality of demographic informational items associated with the user.

Abstract: The present disclosure provides an exemplary method, system, and computing device that may include the steps of receiving a first indication that information of a user has been detected at one or more dark web resources; classifying the item of the compromised information into an information type category; receiving a permission indicator to detect communications by the computing device; receiving a second indication of a communication; receiving a third indication that the user engages an interaction with the communication; instructing the computing device to execute a technique to obtain data for the communication; receiving the data for the communication; determining the communication is a spam communication; determining a current information type category being discussed during the spam communication; making a determination that the current information type category corresponds to the information type category; and instructing a graphical user interface to display an alert to 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.