Abstract: The present disclosure relates to systems and method for securely entering a confidential access code into a user device. A system for allowing secure entry of a confidential access code into a user device may include one or more memories storing instructions and one or more processors configured to execute instruction to perform operations. The operations may include receiving a request for confidential access, prompting the user, via the user interface, to enter a group of inputs into a single-entry field, receiving a dummy sequence of inputs, receiving or providing an indicator signal, receiving an access sequence of inputs, parsing the group of inputs received to identify the access sequence of inputs based on the location of the indicator signal, comparing the access sequence of inputs to the confidential access code associated with the user, and granting or denying access to the confidential information based on the results.

Abstract: Aspects described herein may allow for the application of machine learning techniques to the classification of transactions and/or the prediction of shipping means for such transactions. This may have the effect of generating better insights into user shopping behavior and providing users with better predictions as to how they may expect to receive their purchases. A browser application may be monitored to determine that a user is shopping. A machine learning model may be used to predict shipping data associated with a product and/or merchant associated with the shopping of the user. Predicted shipping data may be displayed to the user, and user feedback may be requested and received to confirm model predictions and review purchases and shipping experiences.

Abstract: In order to facilitate machine learning for prediction using distinct dataset types, systems and methods include collecting content information from archived websites databases. Collecting historical event information from online sources, where the historical event information is associated with a plurality of historical events. Generating event-dependent products training datasets based on the content information and the historical event information, where the event-dependent content training datasets defines for content historical events that are associated with attributes of the content, attribute change of the content, or both. Training an attribute prediction machine learning model based on the event-dependent content training datasets. Applying the trained attribute prediction machine learning model to additional event information to predict, for content, a future attribute estimate, a future attribute change estimate, or both.

Abstract: A device may receive event data associated with a calendar event of a digital calendar of a user and determine, using a machine learning model, a transaction score associated with a probability that the calendar event involves an event transaction. The machine learning model may be trained based on calendar data associated with historical calendar events and transaction data associated with historical transactions. The device may cause, based on the transaction score satisfying a score threshold, the machine learning model to analyze a transaction log of a transaction account of the user to identify transaction information associated with the calendar event. The device may perform an action associated with the transaction information and the calendar event.

Abstract: A method includes displaying on a graphic user interface (GUI) of a computing device of a user, a log of computing operations performed by the user at computing terminals of entity servers respectively managed by entities. The user uses a unique authorization identifier provided by the authorizing entity to authorize the computing operations at the computing terminals of the entity servers. The user provides a fraud indication through the GUI that at least one computing operation in the log is fraudulent. Memory jogging visual units are displayed on the GUI to the user that cause the user to recall performing the at least one computing operation identified as being fraudulent. An entry of the at least one computing operation in an operation database is marked as a valid operation authorized by the user when receiving a recognition indication and potentially fraudulent when no recognition indication by the user.

Abstract: Embodiments of the present disclosure relate to systems and methods for capturing information. In addition, embodiments of the present disclosure relate to solutions for capturing information using a web browser extension. Embodiments of the present disclosure further relate to securely transmitting captured information to a server for association with an application or form being completed by an individual.

Abstract: A device may receive, from a user device, credentials for a transaction account associated with a user. The device may provide, to a merchant device, authorization information enabling access to an indication of an incentive status associated with the user and the transaction account, wherein the incentive status indicates whether an incentive is available for the user. The device may receive, from the merchant device, an incentive status request including the authorization information. The device may determine that the authorization information is valid and is associated with the transaction account. The device may determine a current incentive status for the incentive, wherein the current incentive status is based on a satisfaction of a condition associated with the transaction account. The device may provide, to the merchant device, the indication of the current incentive status for the incentive.

Abstract: The disclosure relates to initiating and completing peer to peer payments or peer to merchant payments initiated by financial cards and cameras of mobile devices. In some embodiments, the disclosed systems and methods may provide an accurate determination of parties involved in a peer to peer transaction, be useable with hardware and software that users are already in possession of, and securely initiate a peer to peer transaction. The disclosed systems may include a server communicatively coupled to a mobile device by a network. The server may receive an image of one or more financial cards, retrieve account information for each financial card in the received image, generate a proposed transaction based on the retrieved account information, transmit the proposed transaction to the mobile device, receive approval for the proposed transaction and initiate a payment or a transfer to an account based on the retrieved account information.

Abstract: The communication techniques described herein may allow a server to send communications to user devices in response to detecting a user interacting with an application and/or their device. A request to access information associated with an account may be detected. Next, a determination may be made as to whether the request is from a third-party and/or initiated by the user. When the request is from a third-party and/or initiated by the user, a customer outreach module may be triggered, which may select a communication to send to the user. A verification of whether the user allows for receipt of communications may be performed. When the user accepts communications, the communication may be sent to the user device. Accordingly, sending communications when the user may be interacting with an application and/or device improves the probability of the user interacting with the communication and the user receiving the information associated with the communication.

Abstract: Systems and methods of the present disclosure include at least one processor that receives electronic messages, each associated with a respective originating entity. Message metadata of each electronic message is extracted and list subscriptions are determined based on the message metadata. User interaction commands relative to each electronic message are tracked. Record similarity values for user activity records are determined based on a measure of similarity between the originating entity and each activity record. An engagement model is utilized to determine an engagement score indicative of a degree of user engagement with each originating entity based on the user activities and the user interaction metrics to generate a prediction indicative of the degree of user engagement. An unsubscribe condition is determined based on the engagement score being below a threshold score, and an unsubscribe command is automatically issued.

Abstract: A device may determine a multi-customer offer, wherein the multi-customer offer identifies a threshold quantity of customers of the multi-customer offer to allow access to the multi-customer offer. The device may receive one or more requests to accept the multi-customer offer, wherein a request, of the one or more requests, is associated with a customer device of the one or more customer devices. The device may determine, based on a quantity of requests of the one or more requests, that the threshold quantity of customers is not satisfied. The device may identify, based on the threshold quantity of customers not being satisfied, one or more candidate customer devices having a likelihood of requesting to accept the multi-customer offer, wherein the one or more candidate customer devices are associated with respective users that are the potential customers, and wherein the likelihood satisfies a threshold.

Abstract: Methods and systems are described for verifying an identity of a user through contextual knowledge-based authentication. The system described uses contextual knowledge-based authentication. By verifying an identity of a user through contextual knowledge-based authentication, the verification is both more secure and more intuitive to the user. For example, by relying on confidential and/or proprietary information, the system may generate verification questions, the answers to which are known only by the user.

Abstract: An example transaction analysis platform is described herein. The transaction analysis platform may analyze an audio signal to determine whether a user is associated with a transaction. The transaction analysis platform may determine that a tone of the audio signal is a transaction tone associated with the transaction. The transaction analysis platform may process, based on detecting the transaction tone, the audio signal to identify a set of tones associated with the transaction. The transaction analysis platform may generate, based on a quantity of the set of tones, transaction information associated with the transaction, wherein the transaction information identifies a quantity of items associated with the transaction, wherein the quantity of items corresponds to the quantity of the set of tones. The transaction analysis platform may perform an action associated with the transaction information and a transaction account of the user.

Abstract: A device may access payment data of a first payment account and a second payment account that are associated with a first user and a second user, respectively, wherein the payment data includes descriptions of payments to vendors. The device may group the vendors into subsets of vendors based on characteristics of the vendors. The device may determine relative weightings for respective vendors within the subsets. The device may receive a request for a vendor recommendation for a meeting between the first user and the second user, wherein the request identifies parameters for the meeting. The device may generate the vendor recommendation for the meeting by identifying candidate vendors based on the parameters for the meeting and selecting one or more of the candidate vendors based on the relative weightings for the respective vendors.

Abstract: A method, system, and non-transitory computer readable medium are disclosed for recommending merchants. The method includes storing a first tip amount and a second tip amount corresponding to a first merchant and a second merchant, respectively. Then, categorizing the first tip amount and the second tip amount into a lower weighted category or higher weighted category. The method also includes generating, using a weighted model that includes machine learning logic: a first score for the first merchant based on the first tip amount and the category of the first tip amount; and a second score for the second merchant based on the second tip amount and the category of the second tip amount. Tip amounts in the higher weighted category carry more weight in generating scores than those in the lower weighted category. Then, either the first merchant or the second merchant is recommended based on the scores.

Abstract: Systems and methods for user authentication using an authentication sequence are disclosed. The disclosed systems and methods may include a computing device for authenticating a user. The computing device may include at least one processor and at least one computer-readable medium. The at least one computer-readable medium can containing instructions that, when executed by the at least one processor, cause the computing device to perform operations. The operations may include receiving an authentication sequence provided by one or more authentication objects and authenticating the user based on the authentication sequence. The authentication sequence can be a card-swipe sequence and authentication can be performed based on characteristics of the card-swipe sequence, such as the cards used, the order the cards are used in the sequence, the timing of card use, and the type of card swipe.

Abstract: Multifactor authentication techniques described herein may allow a user to submit a recent proof of purchase as a part of a multifactor authentication process to access an account associated with a financial institution. As part of the login process, the user may submit a proof of purchase associated with a transaction. The financial institution may determine information associated with the transaction, such as a merchant associated with the proof of purchase, a time of the transaction, the last four numbers of the transaction card used, a dollar amount, or any combination thereof. If the information matches one or more records in the transaction history of the user's account, the financial institution may authenticate the user and provide access to the account. In this way, the financial institution may leverage transaction history known to the financial institution and the user to authenticate the user.

Abstract: Account functionality may be modified in response to an event. A processor may determine that a status of at least one of a user, a device associated with the user, and data associated with the user is potentially insecure due to the event. The processor may identify an account of the user. The account may provide access to electronic financial transaction functionality. In response to the determining that the status of at least one of the user, the device associated with the user, and the data associated with the user is potentially insecure, the processor may modify a condition affecting the access to the electronic financial transaction functionality.

Abstract: Methods and systems are described for verifying an identity of a user through contextual knowledge-based authentication. The system described uses contextual knowledge-based authentication. By verifying an identity of a user through contextual knowledge-based authentication, the verification is both more secure and more intuitive to the user. For example, by relying on confidential and/or proprietary information, the system may generate verification questions, the answers to which are known only by the user.

Abstract: Systems and methods of the present disclosure include at least one processor that receives electronic messages, each associated with a respective originating entity. Message metadata of each electronic message is extracted and list subscriptions are determined based on the message metadata. User interaction commands relative to each electronic message are tracked. Record similarity values for user activity records are determined based on a measure of similarity between the originating entity and each activity record. An engagement model is utilized to determine an engagement score indicative of a degree of user engagement with each originating entity based on the user activities and the user interaction metrics to generate a prediction indicative of the degree of user engagement. An unsubscribe condition is determined based on the engagement score being below a threshold score, and an unsubscribe command is automatically issued.