Machine Learning Model for Predicting Entity Closing Process Issues
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The closing process for entities like corporations and government agencies is complex, leading to resource wastage due to delays and inefficiencies in meeting accounting cycle deadlines, as multiple divisions worldwide perform activities that require extensive monitoring and coordination, resulting in computing, network, and communication resource overutilization.
Innovation Solution
A prediction system utilizing a machine learning model identifies tasks relevant to the closing process, generates requests for historical data, trains on this data to predict potential issues, and provides proactive recommendations to prevent delays, thereby conserving resources and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If extensive monitoring and coordination are performed across multiple worldwide divisions, then the reliability of meeting accounting cycle deadlines is improved, but the use of computing, network, and communication resources increases excessively
Solution Approach 1:
The system enables self-service by allowing the closing process monitoring to operate autonomously through automated data collection from multiple divisions, machine learning-based issue prediction, and automatic generation of recommendations without requiring extensive human coordination and monitoring across worldwide divisions
Solution Approach 2:
The patent replaces the mechanical system of human monitoring and coordination with an automated electronic system that uses machine learning models to predict issues and generates recommendations automatically, substituting manual processes with algorithm-driven automation
2Productivity
If traditional closing process monitoring is performed manually across multiple divisions, then coordination can be maintained, but productivity and efficiency deteriorate due to resource overutilization and delays
Solution Approach 1:
The system performs preliminary action by proactively predicting potential issues before they occur in the closing process. The machine learning model analyzes historical data and current status to forecast problems in advance, allowing preventive measures to be taken before delays actually happen
Solution Approach 2:
The system implements feedback by continuously monitoring the closing process status, comparing it against historical patterns, and generating recommendations based on predicted issues. This closed-loop feedback mechanism enables real-time adjustments to improve productivity and reduce delays
Data Source
AI summary
A device may identify tasks relevant to closing processes for an entity during a period of time, and may generate a request for historical closing data associated with the tasks relevant to the closing processes. The device may provide the request to a server device associated with the entity, and may receive the historical closing data based on providing the request. The device may train a machine learning model, based on the historical closing data, to generate a trained machine learning model, and may receive current closing data associated with tasks relevant to a current closing process. The device may process the current closing data, with the trained machine learning model, to predict issue data identifying at least one potential issue associated with the current closing process, and may provide the issue data to a user device associated with the at least one potential issue.