AI Accounts Receivable Management System
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Solution Overview
Problem
Managing consumer relationships across multiple business partners is technologically challenging and resource-intensive, leading to increased costs and inefficiencies due to the use of independent management systems.
Innovation Solution
An artificial intelligence system that utilizes machine learning to predict future actions related to accounts receivable by generating models based on client entity actions and characteristics, clustering entities with common characteristics, and providing predictive analytics through a CRM system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If independent management systems are implemented for different management functions, then specific management tasks can be handled, but system complexity and costs increase
Solution Approach 1:
The patent combines multiple independent management systems (customer relationship management, account receivable management, marketing management) into a single integrated system. This integration allows the system to handle diverse management tasks through unified data structures and processing logic, reducing overall system complexity while maintaining comprehensive management capabilities.
Solution Approach 2:
The patent creates a universal management system that can perform multiple functions including customer relationship management, account receivable tracking, and marketing operations. The system uses a common data model and processing framework that can be applied across different management domains, eliminating the need for separate specialized systems.
2Measurement precision
If comprehensive data collection and analysis is performed on client entities, then prediction accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary data processing by pre-grouping client entities based on their characteristics and payment behaviors. This pre-processing organizes data into manageable clusters before prediction analysis, reducing the computational burden during actual prediction while maintaining accurate insights into client payment patterns.
Solution Approach 2:
The patent segments the client entity population into distinct groups based on shared characteristics and payment behaviors. This segmentation divides the large-scale data analysis problem into smaller, more manageable segments that can be processed more efficiently, reducing overall processing time while preserving prediction accuracy for each segment.
3Reliability
If machine learning models are generated and updated continuously, then prediction reliability improves, but computational cost and processing time increase
Solution Approach 1:
The patent implements periodic updates of machine learning models rather than continuous updates. The system generates and updates models at scheduled intervals based on accumulated data, maintaining reliable predictions while reducing computational overhead. This periodic approach balances model accuracy with resource consumption.
Data Source
AI summary
An artificial intelligence system for managing a consumer relationship is provided. The artificial intelligence system includes: one or more client entities; a storage device configured to store data related to actions and characteristics of the one or more client entities, the actions and characteristics relating to payment of accounts receivable; one or more machine learning server devices configured to generate and update a machine learning model based on previous actions and characteristics of said one or more client entities; and predict future actions of the one or more client entities with respect to the payment of accounts receivable, based on the machine learning model.