A mechanism for reconciling unreconciled bank statement lines with corresponding accounting book entries in an online bookkeeping service. A machine-configurable similarity measurement model is applied to quantify similarity of an unreconciled bank statement line to each of a set of historical bank statement line reconciliations for a specific user, to identify a most similar line reconciliation, and upon satisfaction of a threshold, to automatically reconcile the unreconciled bank statement line with extracted accounting book entry details from the identified reconciliation.

A computing system may include a network circuit configured to communicate with a third party computing device via a network, one or more interface elements (e.g., application programming interfaces and/or software development kits) providing account open functionality, and an account open circuit configured to: accept, via the interface elements, a set of user data and an account open request for opening a new account with the provider, where the set of user data is received from a user computing device of a user via a third party website or application; establish the new account by generating a set of account data associated with the new account without directing the user to the operating environment of a website or application of the provider; and transmit the set of account data to the third party computing device for use by the third party computing device in a transaction with the user.

Computer systems, apparatuses, processors, and non-transitory computer-readable storage devices configured for executing a method for generating proactive advisor recommendation using artificial intelligence. The method has the steps of: partitioning a plurality of clients using a clustering model based on data of the plurality of clients for clustering the plurality of clients into a plurality of client clusters; classifying the clients of at least a first client cluster of the plurality of client clusters into a plurality of client classifications by using one or more random-forest classifiers; and generating financial recommendations for the clients of at least a first client classification of the plurality of client classifications.

Systems, methods, and non-transitory media for invoking account open functionality via encoded data transmission are disclosed. A service provider computing system can receive access to an application programming interface (API) of an institution computing system. The API is configured to invoke account open functionality. The service provider computing system can provide, to a user device, a website or application configured to facilitate transmission of encoded data to the service provider computing system. The service provider computing system can receive the encoded data from the user device and transmit the encoded data to the institution computing system via the API to invoke the account open functionality of the institution computing system. The encoded data can be configured such that it cannot be decoded by the service provider computing system but can be decoded by the institution computing system.

A system and method are configured to perform real-time financial sweeps management. The system includes a processor, a memory, and a set of modules including a sweeps processing module, a sweeps allocation module, a file processing module, and a transaction pre-processing module. The sweeps processing module includes a policy-implementing pipeline. The file processing module process files including client information, and loads the processed files into the sweeps processing module. The transaction pre-processing module processes received financial transactions, formats the financial transactions, and loads the formatted financial transactions into the sweeps processing module. The sweeps processing module applies the processed files and the formatted financial transactions to the policy-implementing pipeline to generate sweeps allocation data. The sweeps allocation module performs an allocation of financial values to implement a financial sweep. The financial transactions can be reprocessed to roll back the financial transactions. The method implements the system.

A method of constructing a set of motifs for use in detecting messages of interest in a network of nodes is provided, the method comprising controlling circuitry to: acquire target data, the target data comprising a set of messages which have been exchanged between nodes in the network, the set of messages including a number of messages of interest; acquire control data, the control data comprising a set of messages which have been produced based on a random exchange of messages between nodes in the network; detect motifs within the target data and the control data, each motif being a repeated pattern of messages appearing within either the target data and/or the control data; generate a set of values indicative of a significance of the motifs which have been detected in the target data and the motifs which have been detected in the control data using a frequency with which these motifs have been detected; and construct a set of motifs for use in detecting messages of interest in the network using the set of values which have been produced and a comparison of the motifs detected in the target data with the motifs detected in the control data.

Methods for using and training a classifier to predicting user intentions as the user navigates in a digital banking application. A pathway dataset, excluding financial information, is acquired, capturing a sequence of navigation events during a user session. These navigation events, representing user interactions with the application interface, are converted into a sequence of latent space representations and input into a classifier. The classifier is trained using a historical dataset of navigation events annotated with intention labels as ground truth. Upon identifying a predicted user intention, the method suggests a corresponding service via the graphical user interface (GUI). This method ensures accurate intention prediction without accessing sensitive financial information, so that privacy and usability in digital banking environments are enhanced.