Abstract: A device receives first transaction information associated with a first transaction, and a first transaction account utilized for the first transaction and associated with a first financial institution. The device determines, based on a fraud model, that the first transaction is to be denied due to potential fraud associated with the first transaction account and receives second transaction information associated with a second transaction, and a second transaction account utilized for the second transaction and associated with a second financial institution. The device processes the first transaction information and the second transaction information, with a matching model, to determine whether the first transaction information matches the second transaction information and determines that the first transaction was incorrectly denied when the first transaction information matches the second transaction information within a predetermined threshold.

Abstract: A device receives a transaction request concerning a transaction and a transaction card from a transaction terminal, and determines, based on the transaction request, information concerning the transaction card. The device sends, based on the transaction request and to a user device, a query concerning possession of the transaction card by a user of the user device, where the query includes an instruction for the user to confirm possession of the transaction card via a component of the user device. The device receives, from the user device and after sending the query to the user device, a response concerning possession of the transaction card by the user, and determines, based on the response and the information concerning the transaction card, whether the user has possession of the transaction card. The device performs, based on determining whether the user has possession of the transaction card, at least one action.

Abstract: Disclosed herein are system, method, and computer program product embodiments for recommending a service provider. An embodiment operates by receiving a search request specifying a service request by a user and location for the requested service, retrieving prior financial transactions of the user to purchase the service from a database, and determining a preference of the user based on the retrieved prior financial transactions. The embodiment further operates by applying the preference to a model trained to output a recommended service provider and selecting the recommended service provider for display on a user's device. The model more likely recommends service providers in the location having a higher ratio of tip-to-price relative to other providers for the service in the location based on a history of prior financial transactions of a plurality of users.

Abstract: A device receives a transaction request concerning a transaction and a transaction card from a transaction terminal, and determines, based on the transaction request, information concerning the transaction card. The device sends, based on the transaction request and to a user device, a query concerning possession of the transaction card by a user of the user device, where the query includes an instruction for the user to confirm possession of the transaction card via a component of the user device. The device receives, from the user device and after sending the query to the user device, a response concerning possession of the transaction card by the user, and determines, based on the response and the information concerning the transaction card, whether the user has possession of the transaction card. The device performs, based on determining whether the user has possession of the transaction card, at least one action.

Abstract: A method receiving an electronic data modification instruction of a virtual record modification to cause at least one data modification to at least one attribute of a virtual record data structure; determining at least one user record associated with the virtual record; determining at least one record modification preference associated with the at least one user record on the at least one modification condition; utilizing a record-specific modification machine learning model to predict at least one record-specific modification in response to the virtual record modification based on the one modification condition and the one record modification preference; generating an authorization instruction to authorize the electronic data modification instruction to cause the modification by instructing at least one database management system to modify the user record according to the record-specific modification; and instructing a computing device to display the record-specific modification to the at least one user reco

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing recommendations for splitting bills. The approaches disclosed include the ability to obtain information about a bill to be split (such as a photo of the bill), and then use several machine learning models to determine the ‘who,’ ‘what,’ and ‘where’ of the underlying transaction. In particular, machine learning models described herein are used to perform facial recognition of a ‘selfie’ taken when a transaction was made against social media accounts to determine participants of the transaction. The machine learning models may also identify expected pricing from data about a merchant associated with the transaction, and expected amounts for each participant based on the expected pricing.

Abstract: A device receives first transaction information associated with a first transaction, and a first transaction account utilized for the first transaction and associated with a first financial institution. The device determines, based on a fraud model, that the first transaction is to be denied due to potential fraud associated with the first transaction account and receives second transaction information associated with a second transaction, and a second transaction account utilized for the second transaction and associated with a second financial institution. The device processes the first transaction information and the second transaction information, with a matching model, to determine whether the first transaction information matches the second transaction information and determines that the first transaction was incorrectly denied when the first transaction information matches the second transaction information within a predetermined threshold.

Abstract: Systems and methods of record annotation via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may comprise: receiving at least one annotating content item being associated with at least one first record of a user; utilizing a trained machine learning model to: i) generate at least one derived annotating content item based at least in part on the at least one annotating content item and data of the at least one first record; ii) identify at least one second record related to the user based at least in part on: the data of the at least one first record and one or both of profile information and context information of the user; and iii) annotate the at least one second record with the at least one derived annotating content item.

Abstract: A device receives first transaction information associated with a first transaction, and a first transaction account utilized for the first transaction and associated with a first financial institution. The device determines, based on a fraud model, that the first transaction is to be denied due to potential fraud associated with the first transaction account and receives second transaction information associated with a second transaction, and a second transaction account utilized for the second transaction and associated with a second financial institution. The device processes the first transaction information and the second transaction information, with a matching model, to determine whether the first transaction information matches the second transaction information and determines that the first transaction was incorrectly denied when the first transaction information matches the second transaction information within a predetermined threshold.

Abstract: A device may comprise a memory and a processor coupled to the memory. The processor may receive transaction information and entity information for a plurality of entities and may generate a first model based on the transaction information, the entity information, and information identifying an event, a theme, or a transaction parameter. The processor may process, using the first model, the transaction information and the entity information to identify a set of related entities and a type of relationship associated with the set of related entities. The processor may determine, using a second model, one or more modifications to a first set of accounts and a second set of accounts associated with the first and second entities based on the type of relationship and may perform one or more actions based on the one or more modifications.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing recommendations for splitting bills. The approaches disclosed include the ability to obtain information about a bill to be split (such as a photo of the bill), and then use several machine learning models to determine the ‘who,’ ‘what,’ and ‘where’ of the underlying transaction. In particular, machine learning models described herein are used to perform facial recognition of a ‘selfie’ taken when a transaction was made against social media accounts to determine participants of the transaction. The machine learning models may also identify expected pricing from data about a merchant associated with the transaction, and expected amounts for each participant based on the expected pricing.

Abstract: Disclosed herein are system, method, and computer program product embodiments for providing recommendations for splitting bills. The approaches disclosed include the ability to obtain information about a bill to be split (such as a photo of the bill), and then use several machine learning models to determine the ‘who,’ ‘what,’ and ‘where’ of the underlying transaction. In particular, machine learning models described herein are used to perform facial recognition of a ‘selfie’ taken when a transaction was made against social media accounts to determine participants of the transaction. The machine learning models may also identify expected pricing from data about a merchant associated with the transaction, and expected amounts for each participant based on the expected pricing.

Abstract: A device may comprise a memory and a processor coupled to the memory. The processor may receive transaction information and entity information for a plurality of entities and may generate a first model based on the transaction information, the entity information, and information identifying an event, a theme, or a transaction parameter. The processor may process, using the first model, the transaction information and the entity information to identify a set of related entities and a type of relationship associated with the set of related entities. The processor may determine, using a second model, one or more modifications to a first set of accounts and a second set of accounts associated with the first and second entities based on the type of relationship and may perform one or more actions based on the one or more modifications.

Abstract: Disclosed herein are system, method, and computer program product embodiments for recommending a service provider. An embodiment operates by receiving a search request specifying a service request by a user and location for the requested service, retrieving prior financial transactions of the user to purchase the service from a database, and determining a preference of the user based on the retrieved prior financial transactions. The embodiment further operates by applying the preference to a model trained to output a recommended service provider and selecting the recommended service provider for display on a user's device. The model more likely recommends service providers in the location having a higher ratio of tip-to-price relative to other providers for the service in the location based on a history of prior financial transactions of a plurality of users.

Abstract: A device receives a transaction request concerning a transaction and a transaction card from a transaction terminal, and determines, based on the transaction request, information concerning the transaction card. The device sends, based on the transaction request and to a user device, a query concerning possession of the transaction card by a user of the user device, where the query includes an instruction for the user to confirm possession of the transaction card via a component of the user device. The device receives, from the user device and after sending the query to the user device, a response concerning possession of the transaction card by the user, and determines, based on the response and the information concerning the transaction card, whether the user has possession of the transaction card. The device performs, based on determining whether the user has possession of the transaction card, at least one action.

Abstract: A device may comprise a memory and a processor coupled to the memory. The processor may receive transaction information and entity information for a plurality of entities and may generate a first model based on the transaction information, the entity information, and information identifying an event, a theme, or a transaction parameter. The processor may process, using the first model, the transaction information and the entity information to identify a set of related entities and a type of relationship associated with the set of related entities. The processor may determine, using a second model, one or more modifications to a first set of accounts and a second set of accounts associated with the first and second entities based on the type of relationship and may perform one or more actions based on the one or more modifications.

Abstract: A device receives a transaction request concerning a transaction and a transaction card from a transaction terminal, and determines, based on the transaction request, information concerning the transaction card. The device sends, based on the transaction request and to a user device, a query concerning possession of the transaction card by a user of the user device, where the query includes an instruction for the user to confirm possession of the transaction card via a component of the user device. The device receives, from the user device and after sending the query to the user device, a response concerning possession of the transaction card by the user, and determines, based on the response and the information concerning the transaction card, whether the user has possession of the transaction card. The device performs, based on determining whether the user has possession of the transaction card, at least one action.

Abstract: A messaging platform can automatically detect patterns in messaging behavior of a user, and based on the patterns, create and implement rules for incoming messages or other electronic communications. In response to each incoming message or electronic communication, the messaging platform can classify the message or electronic communication based on the created rules. The messaging platform can also perform various tasks in response to receiving the message or electronic communication. For example, the messaging platform can create a calendar entry in the user's calendar, forward a message, archive a message, reply to a message, etc.

Abstract: A device receives first transaction information associated with a first transaction, and a first transaction account utilized for the first transaction and associated with a first financial institution. The device determines, based on a fraud model, that the first transaction is to be denied due to potential fraud associated with the first transaction account and receives second transaction information associated with a second transaction, and a second transaction account utilized for the second transaction and associated with a second financial institution. The device processes the first transaction information and the second transaction information, with a matching model, to determine whether the first transaction information matches the second transaction information and determines that the first transaction was incorrectly denied when the first transaction information matches the second transaction information within a predetermined threshold.

Abstract: A device receives a transaction request concerning a transaction and a transaction card from a transaction terminal, and determines, based on the transaction request, information concerning the transaction card. The device sends, based on the transaction request and to a user device, a query concerning possession of the transaction card by a user of the user device, where the query includes an instruction for the user to confirm possession of the transaction card via a component of the user device. The device receives, from the user device and after sending the query to the user device, a response concerning possession of the transaction card by the user, and determines, based on the response and the information concerning the transaction card, whether the user has possession of the transaction card. The device performs, based on determining whether the user has possession of the transaction card, at least one action.