AI User Activity Modeling for Faster Financial Closing
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Solution Overview
Problem
The financial closing process in accounting is faced with a tradeoff between accuracy and efficiency, where detailed processes lead to longer completion times, delaying decision-making and straining resources, while less detailed processes may compromise accuracy and compliance.
Innovation Solution
A multi-layer neural network is trained to predict user interface navigation and data slices, using machine learning to suggest next actions and reduce the need for manual data entry, thereby optimizing the closing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a detailed financial closing process is implemented to ensure accuracy and compliance, then manufacturing precision and reliability are improved, but the duration of action and productivity deteriorate due to extended completion times and increased resource consumption
Solution Approach 1:
The patent replaces manual mechanical processes (accountants manually navigating through financial systems, entering data, and performing reconciliations) with an automated AI system that uses machine learning models to predict user actions, automatically perform data entry, and suggest reconciliations. This substitution dramatically reduces completion time while maintaining accuracy through consistent automated execution of standardized procedures
Solution Approach 2:
The system performs preliminary actions by predicting and preparing financial closing activities in advance. The AI model analyzes historical data and current financial state to pre-calculate reconciliations, pre-identify required data, and prepare adjustment entries before the actual closing process begins, thereby reducing the time and effort needed during the actual closing period
2Reliability
If a detailed financial closing process is implemented to minimize errors and ensure compliance, then reliability is improved, but productivity deteriorates due to increased labor costs and resource strain
Solution Approach 1:
The system enables self-service by allowing the AI to autonomously perform repetitive financial closing tasks such as data entry, account reconciliations, and preliminary review without requiring continuous human intervention. The machine learning model learns from historical user behaviors and automatically executes appropriate actions, reducing the need for additional accounting personnel while maintaining reliable compliance through automated checklist-based procedures
Solution Approach 2:
The system implements continuous feedback loops where the AI model learns from actual user corrections and adjustments made during financial closing processes. This feedback mechanism allows the system to improve its predictions and automate increasingly complex tasks over time, thereby enhancing productivity while maintaining or improving reliability through iterative learning from real-world compliance scenarios
Data Source
AI summary
Systems, methods, and computer-readable media are provided for training and using a multi-layer neural network to detect a set of most likely action tuples each comprising a next user interface, a next operation, and a next data slice at least in part by training the multi-layer neural network to predict sequentially next user input and sequentially previous user input for adjacent groups. An example method may include providing a particular data structure as input to the multi-layer neural network to predict a particular action tuple comprising a next particular operation, a next particular user interface, and a next particular data slice to be used by a particular user as the particular user navigates a particular user interface. The method may also include causing display of a summary of the particular action tuple and an option to perform a particular user interface navigation to a particular user navigation target.