Probit Model for Probate Prediction in Debt Collection
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Solution Overview
Problem
Debt collection is inefficient due to the difficulty in determining whether a deceased debtor has an estate, as existing methods are time-consuming and resource-intensive, especially with varying court processes across multiple jurisdictions.
Innovation Solution
A computer-assisted method using a probit model that selects a subset of credit, demographic, and personal variables from historical data to predict the likelihood of an estate existing, allocating resources based on a score indicating the probability of estate presence, thereby optimizing collection efforts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual methods are used to determine estate presence, then flexibility in handling complex court processes is maintained, but time consumption and resource intensity increase significantly
Solution Approach 1:
The patent replaces manual investigative processes with an automated probit model that uses credit data, demographic information, and personal characteristics to predict estate presence. This substitution eliminates time-consuming manual searches while maintaining operational flexibility through configurable model parameters and adjustable prediction thresholds.
Solution Approach 2:
The patent introduces a probit model as an intermediary between collection agencies and court systems. This model processes available data to generate probability scores, serving as a mediator that reduces the need for direct manual engagement with complex court processes while still achieving accurate estate identification.
2Measurement precision
If comprehensive manual searches are conducted for estates, then detection accuracy is improved, but resource allocation efficiency deteriorates
Solution Approach 1:
The patent changes the parameters for estate detection from manual search criteria to statistical probability scores based on multiple variables. By transforming detection into a parameter-based prediction system using credit data, demographics, and personal characteristics, the system achieves high detection accuracy while optimizing resource allocation through probability-driven prioritization.
Solution Approach 2:
The patent segments the estate detection process into independent variable analysis (credit data, demographic information, personal characteristics) and integrates them through the probit model. This segmentation allows for efficient processing of individual factors while maintaining comprehensive detection capability through the combined probability assessment.
3Reliability
If specialized collection agencies are engaged for deceased debtors, then collection effectiveness is improved, but operational complexity and cost increase
Solution Approach 1:
The patent enables collection agencies to perform estate detection independently using the probit model, eliminating the need for specialized collection agencies. The self-service approach uses readily available data (credit reports, demographic information, personal characteristics) to generate predictions, reducing operational complexity while maintaining collection effectiveness through automated probability-based identification.
Data Source
AI summary
A computer assisted method includes selecting a sample of accounts from a historical database of accounts corresponding to deceased debtors, The sample indicates whether or not an estate was found for each deceased debtor. A comprehensive set of credit related variables corresponding to the accounts is obtained from a first source. A comprehensive set of demographic related variables corresponding to the accounts is obtained from a second source. The variables are mapped to the accounts and a computer executable model is created by identifying a subset of variables from the sets of variables and using the subset of variables and sample of accounts such that the model provides a prediction of whether or not an estate exists for a given deceased debtor.