AI-Driven Collection Customer Prioritization System
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Solution Overview
Problem
Current accounts receivable management and collections processes are inefficient due to a lack of data-driven approaches, leading to ineffective collection strategies and worklist generation, which results in suboptimal collection efforts and high resource intensity.
Innovation Solution
An automated system that uses machine learning and artificial intelligence to generate prioritized lists of collection customers based on impact scores and optimize collection agent worklists, incorporating real-time data analysis and trend identification to improve collection efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated systems use machine learning and AI to generate prioritized lists of collection customers, then collection efficiency and productivity are improved, but device complexity and implementation difficulty increase
Solution Approach 1:
The system segments the collections management process into distinct functional modules: data collection module, machine learning model module, prioritization ranking module, and worklist generation module. Each module performs a specific function, making the complex automated system more manageable and implementable while maintaining high collection efficiency through coordinated operation of these segmented components
Solution Approach 2:
The patent introduces an intermediary processing layer between raw collection data and final collection decisions. This intermediary layer includes feature engineering components, model training pipelines, and result validation mechanisms that bridge the gap between complex AI/ML processing and practical collections operations, reducing implementation difficulty while preserving productivity benefits
2Productivity
If data-driven approaches are implemented for collection strategies and worklist generation, then collection results and agent productivity improve, but loss of time for data processing and analysis increases
Solution Approach 1:
The system performs preliminary actions by pre-collecting and pre-processing collection data, pre-training machine learning models with historical data, and pre-generating prioritization rankings before actual collections activities begin. This advance preparation reduces the time required for data processing during active collections operations, allowing agents to immediately utilize pre-computed worklists and prioritizations without experiencing time delays
Solution Approach 2:
The patent implements continuous data processing and model updating mechanisms that operate in the background during collections activities. Rather than stopping to process data, the system continuously ingests new collection outcomes, updates models in real-time, and maintains current prioritizations, ensuring that useful actions (data analysis and model improvement) continue without interrupting collections productivity
3Reliability
If opinion-based collection strategies are replaced with objective analytical approaches, then reliability and accuracy of collection decisions improve, but device complexity and analytical requirements increase
Solution Approach 1:
The analytical system performs self-service by automatically collecting relevant data, automatically engineering features, automatically training and selecting models, and automatically generating prioritization rankings without requiring external analytical intervention. This self-service capability maintains high decision accuracy through objective analytical approaches while reducing the complexity burden on users, as the system handles its own analytical processing autonomously
Solution Approach 2:
The patent employs parameter changes by systematically varying and optimizing multiple parameters including data selection criteria, feature engineering parameters, model hyperparameters, and weighting schemes for different data sources. This structured parameter optimization process improves decision accuracy through objective analytical comparison of different configurations while managing complexity through automated parameter tuning rather than manual analytical complexity
Data Source
AI summary
Disclosed embodiments provide tools and techniques for the automated prioritized ranking of collection customers for accounts receivable and collections management processes. In some embodiments, one or more computing systems may repetitively generate a prioritized list of collection customers requiring collection activity. The prioritized lists can be generated at selected time intervals, for example daily, weekly, or monthly. The generation of the prioritized list for a selected time interval can include several processing and monitoring steps implemented with machine learning or artificial intelligence. The prioritized list may then be provided to collections agents, collections managers, or downstream software modules.