Essentia-Based Debtor Mapping for Dunning Effectiveness
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Solution Overview
Problem
Existing dunning methods struggle to effectively adapt to complex communication networks and improve overall dunning effectiveness.
Innovation Solution
The implementation of an essentia-based dunning system, which maps debtors to essentia instances based on their characteristic variables, determines essentia-specific dunning decisions, and applies these decisions to dunning messages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional dunning methods are used, then the process is simple to implement, but the effectiveness of dunning is low
Solution Approach 1:
The patent segments debtors into different essentia categories based on their characteristic variables (demographics, behavior, preferences). This segmentation allows the system to apply different dunning strategies to different groups, improving overall effectiveness while managing complexity through structured categorization
Solution Approach 2:
The patent implements local quality by tailoring dunning communications to match the specific characteristics of each essentia category. Each category receives customized messaging, timing, and channel selection based on their unique attributes, thereby improving effectiveness without requiring complete system redesign
2Reliability
If communication networks are expanded to improve dunning, then dunning effectiveness improves, but the complexity of communication networks increases
Solution Approach 1:
The patent applies dynamics by making the communication network adaptable through essentia-based decisioning. The system dynamically selects communication channels, timing, and message content based on real-time essentia categorization, allowing the network to adapt to different debtor characteristics without requiring permanent infrastructure for every possible scenario
Solution Approach 2:
The patent implements universality by creating a multi-functional communication framework that can handle multiple dunning scenarios through a single essentia-based system. The same infrastructure serves diverse communication needs by adapting to different essentia categories, reducing the need for separate specialized systems
Data Source
AI summary
Dunning a debtor according to an essentia instance of the debtor. Debtor data of the debtor is received from a creditor. The debtor is mapped to an essentia to create an essentia instance for the debtor based on debtor characteristic variables identified by the debtor data and essentia characteristic variables identified by essentia data. Essentia specific dunning decisions corresponding to the essentia to apply when dunning the debtor are determined. The debtor is dunned according to the essentia specific dunning decisions.