Collections Model Using Tax Data Segmentation
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Solution Overview
Problem
Revenue agencies face inefficiencies in collecting accounts due to the inflexibility of traditional credit scoring methods, which do not adequately consider a debtor's willingness or ability to pay, often requiring external collection services for stale accounts.
Innovation Solution
A collections model is developed that combines raw credit data and tax form data to generate a collections score, allowing for tailored treatment approaches based on score bands, enabling revenue agencies to prioritize and manage accounts more effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single inflexible workflow is used to process all accounts, then the collections process is simple to manage, but the ability to identify and prioritize accounts based on debtor's willingness or ability to pay is poor
Solution Approach 1:
The patent segments the collections workflow into multiple pathways based on FICO score bands (e.g., 25-point intervals from 200-225, 225-250, etc.). Each score band receives customized treatment, allowing the system to precisely identify and prioritize payable accounts while maintaining manageable complexity through structured segmentation.
Solution Approach 2:
The patent changes the parameter of credit score thresholds to create discrete segments. By establishing specific FICO score bands (e.g., 200-225, 225-250, 250-275), the system transforms continuous credit data into actionable categories that drive different collection strategies, improving identification precision without overwhelming complexity.
2Productivity
If external collections services are used for all accounts, then collection coverage is comprehensive, but the cost and time loss increase due to accounts becoming stale
Solution Approach 1:
The patent applies preliminary action by determining the appropriate collections pathway before accounts become stale. By using FICO scores to pre-categorize accounts into score bands and assign them to suitable collection methods (internal vs. external services), the system acts in advance to prevent time loss and improve productivity.
Solution Approach 2:
The patent enables self-service by allowing debtors with higher FICO scores (indicating willingness/ability to pay) to receive automated or self-directed collection treatments. This reduces the need for immediate external service intervention, improving efficiency while preventing account staleness.
3Adaptability or versatility
If FICO score bands are used to segment accounts, then the ability to tailor collection treatment is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent uses parameter changes by establishing fixed FICO score band thresholds (e.g., 200-225, 225-250, 250-275). This transforms complex continuous credit data into discrete, manageable categories that enable customized treatment while controlling model complexity through standardized band definitions.
Solution Approach 2:
The patent segments the credit score range into standardized bands that balance adaptability and complexity. Each band (e.g., 25-point intervals) represents a manageable segment that can be assigned specific collection treatments, providing customization without requiring overly complex processing systems.
4Measurement precision
If traditional credit scoring is used without customization, then the scoring process is straightforward, but the predictive power for identifying payable accounts is insufficient
Solution Approach 1:
The patent enhances predictive power by changing the parameter from a single FICO score to segmented FICO score bands. This segmentation allows the system to apply different collection strategies to different risk levels, improving measurement precision for identifying payable accounts while maintaining straightforward operation through standardized band categories.
Data Source
AI summary
Apparatuses, computer media, and methods for analyzing credit and tax form data and determining a collection treatment type to collect revenue. A collections model is constructed to determine a collections score that is based on raw credit data and tax form data and is indicative of a debtor's propensity to pay an owed amount. The collections model includes score bands, each score band being associated with a range of credit scores. A collections score is determined from a scoring expression that is associated with a score band and that typically includes a subset of available raw credit data and tax form data. A collections treatment type is determined from a collections score. Each treatment type corresponds to a treatment action that is directed to the debtor. A collections model is constructed from historical tax data, in which score bands and scoring expressions are constructed for the collections model.