Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving a notification to engage a transmission guard bot in a communication session; generating a trained transmission guard bot configured with communication capabilities to engage the communication session; engaging the trained transmission guard bot into the communication session between the plurality of computing devices associated with the plurality of users; utilizing the trained transmission guard bot to continually monitor a plurality of content data associated with the communication session for a predetermined period of time; detecting a suspicious content data point within the plurality of content data associated with the communication session based on the continual monitoring of the at least one transmission guard bot; determining a risk assessment metric associated with the communication session; and invoking an action related to spam prevention via the at least one trained transmission guard bot.

Abstract: A system may include a memory storing historical transaction data. A system may include a processor configured to: apply a machine learning algorithm to predict a driving route for an autonomous vehicle based on the historical transaction data; and transmit the predicted driving route to the autonomous vehicle. A system may include the autonomous vehicle configured to. A system may include receive the predicted driving route. A system may include travel along the predicted route; and make one or more product transaction stops along the predicted route.

Abstract: A system may include a user interface configured to receive a user authentication request. A system may include a processor in communication with a server and the user interface, configured to: receive one or more delivery instructions from the server, cause the autonomous vehicle to move to a delivery location based on the one or more delivery instructions, authenticate the user authentication request; and grant user access, based on successful authentication of the authentication request, to one or more partitioned areas of the autonomous vehicle.

Abstract: Systems and methods for detecting abusive behavior. In some aspects, a method includes receiving first transfer data for a first transfer associated with a second category of resources and a first indication whether the first transfer is associated with abusive behavior. The first transfer data may be processed using a first machine learning model to generate a first prediction regarding whether the first transfer is associated with abusive behavior. In response to determining that the first prediction does not match the first indication, the first machine learning model may be updated to generate a second machine learning model for the second category of resources. The method may include receiving second transfer data for a second transfer associated with a second category of resources and processing the second transfer data using the second machine learning model to generate a second prediction regarding whether the second transfer is associated with abusive behavior.

Abstract: Disclosed aspects and embodiments pertain to customer distress assistance. A call can be detected to a callee from a caller. The call can be analyzed and determined to be a fraud or spam risk. In response, callee monitoring is triggered. The monitoring can capture biometrics or speech of the callee. Distress in the callee can be determined through analysis of the biometrics or speech and comparison to reference biometrics or speech of the callee. An individual associated with the callee can be selected based on determining the distress in the customer. The individual can be contacted through a notification or a call to assist the callee in distress.

Abstract: Systems and methods of the present disclosure enable the generation and use of secure offline tokens for account activity authentication. A processor receives an activity record including activity details including an entity identifier. The processor determines a flight booking associated with the activity record when the entity identifier includes an airline, and determines flight data associated with the flight booking. The processor automatically generates an offline token associated with the user account, including account access restrictions. The account access restrictions include a token activation period that binds the offline token to a duration of the flight, and a token entity binding that binds the offline token to use with the airline for performing in-flight account activity requests. The processor automatically causes a computing device to download the offline token to enabling performing the in-flight account activity requests with the entity during the offline token activation period.

Abstract: Systems and methods for secure authentication information retrieval. In some aspects, the system receives a request for accessing authentication information for an authentication token. The system retrieves a user profile for the user comprising token identifiers for authentication tokens associated with the user and authentication information for each authentication token. The system receives an indication that a near field communication (NFC) interaction has been initiated between the user device and the authentication token. The system determines whether the authentication token is associated with the user and renders the authentication information into a graphical overlay. The system may additionally transmit the graphical overlay for displaying the authentication information over an image of the authentication token captured via an imaging sensor included in the user device.

Abstract: Methods and systems are disclosed herein for using one or more machine learning models to determine whether a user is expected to complete a task or action by a deadline. The one or more machine learning models may be trained and/or used to recommend a communication protocol for sending a reminder to the user such that the user is predicted to be more likely to complete an action by the action's deadline. A computing system may use the one or more machine learning models to generate a recommendation for type of reminder to send, for example, if it is predicted that the user is not expected to complete the task by the deadline. A computing system may determine the message to send, the communication protocol to use, and/or the time to send the message.

Abstract: A computer-implemented method for authenticating a source of a telephone call to a recipient of the telephone call may include receiving, prior to receiving the telephone call, a request to input first authentication information. The first authentication information may include a passcode or a selection of a security question. The method may include receiving input of the first authentication information and sending, by the user device, the first authentication information to a computing device associated with the source of the telephone call. The method may include receiving the telephone call from the source and receiving input from the recipient of the telephone call to answer the telephone call. The method may include receiving second authentication information from the computing device, where the second authentication information may include the passcode or an answer to the security question. The method may include outputting the second authentication information.

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: 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: Disclosed embodiments pertain to determining and communicating purchasing power through a visualization. A trained machine learning model can be invoked on a credit card without a preset limit to predict a dynamic spending limit based on credit card data or credit score data. Purchasing power can be computed as the difference between the dynamic spending limit and a current balance, and a purchase power class can be determined based on the purchasing power. Further, a graphic representation of a purchasing power class can be determined. Subsequently, presentation of the graphic representation can be triggered in a manner that overlays the graphic representation on or around the physical card.

Abstract: A method includes generating a set of non-trusted contacts, each contact of the set of non-trusted contacts corresponding to contact information believed to be associated with vishing attacks. The method further includes sending the set of non-trusted contacts to a computing device of a user. The computing device is configured to add the set of non-trusted contacts to a contacts list associated with an audio calling functionality of the computing device. The computing device is configured to receive a call and determine that the call is from a caller associated with a contact in the set of non-trusted contacts. The computing device is configured to silence the call, block the call, or display caller information of the contact based on the determination that the call is from the caller associated with the contact in the set of non-trusted contacts.

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: In some aspects, a computing system may use machine learning to determine whether a control is faulty or generate recommendations to make a modification to a control. may identify a portion of problematic computer code that implements the faulty control through the use of machine learning. A computing system may use machine learning to generate embeddings that map incident data and control data (e.g., computer-readable code of a control) to the same vector space. Further, a computing system may use a weighting mechanism that may be used to weight each sample used to train a machine learning model, which may allow a model to train more efficiently.

Abstract: Systems, apparatuses, and methods are described for adjustment of card configurations for flight interruptions. Past flight interruptions that users experienced may be determined. Transactions conducted via the users' payment cards during the past flight interruptions may be determined. The data may be used for determining customized metrics for adjusting card configurations of the payment cards during current flight interruptions. Default metrics for adjusting card configurations of the payment cards during current flight interruptions may be determined. Current flight statuses may be monitored for the users. Current flight interruptions may be detected for the users. Card configurations of the payment cards may be adjusted during the detected current flight interruptions.

Abstract: Methods, apparatuses and non-transitory media are provided for receiving, via a data sharing communication from a map application executing on a mobile computing device, and via an application on the mobile computing device associated with a user, map application data. The map application data may include a destination. The methods, apparatus and non-transitory media may be further provided for determining, by a machine learning model and based on the received map application data, a function that the user intends to perform at the destination, the machine learning model analyzing past user interactions with an entity associated with the destination, and, based on determining the function the user intends to perform, generating a notification, in the application, of one or more options for performing the function.

Abstract: Systems and methods for external account authentication are disclosed herein. They include receiving a call to pair the external account with a secure account, extracting external data from the external account, the external data corresponding to external account content, providing user activity data from the secure account as an input to an authentication machine learning model, providing the external data as an input to the authentication machine learning model, the authentication machine learning model configured to output a certainty level that the external account is associated with a user of the secure account based on the external data and the activity data, receiving the certainty level from the authentication machine learning model, determining that the certainty level meets a certainty threshold, and pairing the external account with the secure account based on determining that the certainty level meets the certainty threshold.

Abstract: Disclosed embodiments pertain to determining and communicating purchasing power through a visualization. A trained machine learning model can be invoked on a credit card without a preset limit to predict a dynamic spending limit based on credit card data or credit score data. Purchasing power can be computed as the difference between the dynamic spending limit and a current balance, and a purchase power class can be determined based on the purchasing power. Further, a graphic representation of a purchasing power class can be determined. Subsequently, presentation of the graphic representation can be triggered in a manner that overlays the graphic representation on or around the physical card.

Abstract: An exemplary method includes receiving, by a processor, flight information from a computer device associated with an airline identifying a flight and passengers on the flight that purchased flight tickets by utilizing payment cards associated with a card authorizing entity. Passenger food preferences for the flight passengers based at least in part on historical payment card transaction data of the flight passengers is received from a computing device associated with the card authorizing entity based on the flight information. A food inventory available to be serviced on the flight is received. Automatically determining a flight-specific food inventory, to minimize an overall weight of the flight-specific food inventory to be carried by the flight based on the passenger food preferences. Automatically transmitting food packing instructions to a packing-related machine based on the flight-specific food inventory so as to assemble a portion of the flight-specific food inventory.