Abstract: A system and method for securely transferring funds is disclosed herein. A computing system receives a request to transfer funds from a first account associated with the transferor to a second account associated with the transferee. The computing system identifies one or more constraints specified in the request that are associated with the funds. The computing system stores the funds as reserved funds in the second accounts associated with the transferee. The computing system receives, from a third-party vendor, a transaction request for verifying a transaction between the transferee and the third-party vendor. The computing system identifies one or more parameters associated with the transaction request. The computing system determines that the one or more parameters associated with the transaction request are in accordance with the one or more rules related to use of the funds. The computing system grants the transaction request based on the determining.

Abstract: A system and method for routing customers to an automated teller machine (ATM) is disclosed herein. A computing system receives, from a client device, a request to locate an ATM. The request includes a constraint of a desired ATM. The computing system identifies a plurality of ATMs proximate a location of the client device. The computing system pings the plurality of ATMs proximate the location of the client device to identify attributes associated with each respective ATM. The computing system receives the attributes from the plurality of ATMs proximate the location of the client device. The computing system compares the attributes from each respective ATM to historical ATM usage statistics associated with the client device. The computing system routes a user of the client device to a target ATM from the plurality of ATMs based at least partially on the historical ATM usage statistics.

Abstract: Methods and systems are disclosed for allowing an electronic purchase upon completion of an exercise goal and/or a physical activity goal. Exercise and/or physical activity goal criteria can be retrieved for a user. Electronic charge approval criteria can be retrieved for the user, the electronic charge approval criteria based on the exercise and/or physical activity goal criteria. It can be determined whether the exercise and/or physical activity goal criteria has been fulfilled using an electronic measurement system, the electronic measurement system measuring motion data and/or heart rate data for the user. A requested electronic charge can be authorized upon a determination that the electronic charge approval criteria for the user has been fulfilled.

Abstract: A user device may output an indication of an incoming call from a calling device. The user device may receive a request to screen the incoming call. The user device may analyze a transcription of voice input, received from the calling device, for one or more keywords related to a request for funds. The user device may output one or more input options, which permit a user of the user device to respond to the request for funds, including an input option to transfer funds from a first account associated with the user device to a second account associated with the calling device. The user device may detect a user interaction with the input option to transfer funds from the first account to the second account. The user device may transmit a request that causes funds to be transferred from the first account to the second account.

Abstract: Provided are arrangements requiring additional multi-factor authentication (MFA) in certain instances of dynamic virtual transaction token (e.g., virtual credit card token) generation via a browser extension. In the arrangements, the browser extension, when executed, may cause a browser to require a user to complete an initial multi-factor authentication (MFA). Once the browser extension has a record indicating a prior valid initial MFA for the user, the user allowed to request a transaction token. The browser extension may allow issuance of the transaction token upon an indication that the value needed does not exceed the predetermined value.

Abstract: Systems and methods of the present disclosure enable context-aware haptic error notifications. The systems and methods include a processor to receive input segments into a software application from a character input component and determine a destination. A context identification model predicts a context classification of the input segments based at least in part on the software application and the destination. Potential errors are determined in the input segments based on the context classification. An error characterization machine learning model determines an error type classification and an error severity score associated with each potential error and a haptic feedback pattern is determined for each potential error based on the error type classification and the error severity score of each potential error of the one or more potential errors. And a haptic event latency is determined based on the error type classification and the error severity score of each potential error.

Abstract: Various embodiments may be generally directed to techniques for planning currency exchange transactions based on customer-provided budgeting information associated with international travel. In some embodiments, an apparatus may comprise a network interface and processing circuitry coupled to the network interface, and the processing circuitry may be operative to forecast future requirements for one or more currencies based on travel budgeting information associated with scheduled international travel. In an example embodiment, the processing circuitry may receive travel budgeting information indicating one or more budgeted amounts of a first currency, determine a marginal currency requirement projection for a second currency based on the one or more budgeted amounts of the first currency, and update a currency requirement forecast for the second currency based on the marginal currency requirement projection. The embodiments are not limited in this context.

Abstract: A system and method of establishing a communication session is disclosed herein. A computing system receives, from a client device, a content item comprising text-based content. The computing system generates a mark-up version of the content item by identifying one or more characters in the text-based content and a relative location of the one or more characters in the content item. The computing system receives, from the client device, an interrogatory related to the content item. The computing system analyzes the mark-up version of the content item to identify an answer to the interrogatory. The computing system generates a response message comprising the identified answer to the interrogatory. The computing system transmits the response message to the client device.

Abstract: A system and method of establishing a communication session is disclosed herein. A computing system receives, from a client device, a content item comprising text-based content. The computing system generates a mark-up version of the content item by identifying one or more characters in the text-based content and a relative location of the one or more characters in the content item. The computing system receives, from the client device, an interrogatory related to the content item. The computing system analyzes the mark-up version of the content item to identify an answer to the interrogatory. The computing system generates a response message comprising the identified answer to the interrogatory. The computing system transmits the response message to the client device.

Abstract: An income analysis platform may receive information associated with employment-related activity of a user, wherein the employment-related activity is associated with an employment status of the user. The income analysis platform may analyze, using an employment analysis model, the information to determine the employment status of the user. The income analysis platform may determine, based on the information and using the employment analysis model, that there is a change in the employment status of the user. The income analysis platform may identify, based on the determined change in the employment status of the user, a service associated with a transaction account of the user. The income analysis platform may determine, based on the change in the employment status, that one or more terms of the service are to be adjusted. The income analysis platform may perform an action associated with adjusting the one or more terms of the service.

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: An example fraud analysis platform may receive timing information associated with a user interaction during a transaction, wherein the user interaction is associated with a user engaging in a transaction using a transaction account and a transaction device to access the transaction account. The fraud analysis platform may generate, based on the timing information, a transaction interaction signature associated with the user interaction. The fraud analysis platform may compare the transaction interaction signature to a reference signature associated with an authorized user of a transaction account. The fraud analysis platform may determine, based on the transaction interaction signature being associated with the reference signature, that the user interaction was performed by the authorized user of the transaction account. The fraud analysis platform may perform, based on determining that the user interaction was performed by the authorized user, an action associated with the transaction or transaction account.

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: 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 obtain user information associated with a user and first account information associated with the user. The device may determine, based on the user information, user employment information and may determine, based on the first account information, user compensation information. The device may process, using a first machine learning model, the user employment information and the user compensation information to determine predicted future user compensation information. The device may obtain second account information associated with the user and may determine, based on the second account information, new user compensation information. The device may determine whether the new user compensation information is consistent with the predicted future user compensation information. The device may determine a predicted reason for the new user compensation information not being consistent with the predicted future user compensation information.

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 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: A first device may receive, from a second device, a request to approve a transaction wherein the request includes transaction data related to the transaction and an image of a signature of an individual that submitted the request. The first device may determine, after receiving the request, a priority level associated with the transaction based on the transaction data. The first device may process the image of the signature using a computer vision technique and/or a vector-based technique. The first device may select, from a memory storing a plurality of comparator signatures, a comparator signature for the signature based on the priority level. The first device may use the comparator signature to verify the signature to approve or deny the transaction. The first device may perform a comparison of the comparator signature and the signature in the image after processing the image and selecting the comparator signature.

Abstract: A system and method for generating a ledger is disclosed herein. A computing system receives, from one or more third party vendors, a plurality of transactions associated with a user. The computing system parses the plurality of transactions to identify one or more parameters associated with each transaction of the plurality of transactions. The computing system groups the one or more transactions into one or more clusters based on the identified one or more parameters. The computing system associates a life event to each cluster of the one or more clusters. The computing system interfaces with a client device associated with the user to confirm an associated life event. Upon receiving a confirmation from the user regarding the associated life event, the computing system generates a ledger. The ledger includes the life event and the one or more transactions associated therewith.

Abstract: A device may provide, to a user device, task data identifying tasks to be performed, and may receive, from the user device, a selection of a particular task from the tasks to be performed. The device may identify cameras associated with a particular task location, and may receive, from the user device, data identifying a location of the user device. The device may determine that the location of the user device matches the particular task location, and may receive, from the user device, task image data identifying images of the particular task location. The device may access, from the cameras, camera data identifying images of the particular task location, and may process the task image data and the camera data, with a machine learning model, to determine performance data associated with performance of the particular task. The device may perform one or more actions based on the performance data.