Payment Collection Path Using AI Occupant Tiering

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Solution Overview

Problem

Property managers struggle to predict occupant payment reliability and engagement effectively, leading to inefficient rent collection strategies, increased risk of defaults, and missed opportunities for proactive intervention, due to the lack of consideration of non-financial indicators and comprehensive patron profiles.

Innovation Solution

A method and system utilizing a machine learning model to analyze financial and non-financial data to determine payment likelihood and assign tiers, enabling tailored collection actions and proactive strategies.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If property managers use traditional reactive collection strategies without non-financial data, then operational simplicity is maintained, but payment prediction accuracy and collection efficiency deteriorate

Engineering Contradiction:

Improvepayment prediction accuracyVSAvoiddata analysis system complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system segments occupants into different tiers (Tier 1, Tier 2, Tier 3) based on their payment likelihood scores, allowing property managers to apply different collection strategies to different segments. This segmentation resolves the contradiction by enabling accurate payment prediction through multi-factor analysis while maintaining operational simplicity through automated tier-based categorization.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The machine learning model acts as an intermediary that processes multiple data sources (financial data, non-financial data, communication patterns, payment history) and transforms them into actionable tier assignments. This intermediary handles the complexity of data integration and analysis, providing accurate predictions without requiring property managers to directly manage the complex data processing.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Productivity

If property managers pursue rent collection from all occupants uniformly, then comprehensive collection coverage is achieved, but resource efficiency and time utilization deteriorate

Engineering Contradiction:

Improvecollection efficiencyVSAvoidtime spent on unproductive pursuits

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system performs preliminary analysis of occupant data before collection activities begin, assigning tiers and predicting payment likelihood in advance. This preliminary action identifies high-priority occupants who need immediate attention and low-priority occupants who can be monitored passively, eliminating wasted time pursuing occupants unlikely to pay while ensuring focused effort on promising candidates.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system applies partial action by focusing intensive collection efforts only on occupants in specific tiers (e.g., Tier 1 and Tier 2) who show higher payment likelihood, rather than uniformly pursuing all occupants. This selective approach improves productivity by concentrating resources on the most promising cases while reducing time loss on unproductive pursuits of low-likelihood occupants.

Inventive Principle:

Principle #16Partial or excessive action

3Reliability

If property managers lack comprehensive patron profiles with non-financial data, then data collection simplicity is maintained, but ability to identify underlying issues and improve retention deteriorates

Engineering Contradiction:

Improveoccupant retention reliabilityVSAvoidnon-financial data utilization

Core Design Contradiction:

ReliabilityVSLoss of information

Solution Approach 1:

The comprehensive patron profile system serves multiple functions simultaneously: it predicts payment likelihood, identifies communication preferences, detects underlying issues affecting payment behavior, and informs retention strategies. This multi-functional approach improves retention reliability by providing a unified view of each occupant while the automated system handles the complexity of collecting and analyzing diverse non-financial data types.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20260057461A1Method of determining collection path

Publication Date: 2026.02.26 TECHCOLLECT LLC

AI summary

This invention describes an advanced method of assessing payment collection challenges using both artificial intelligence (AI) and machine learning (ML) techniques. The system analyzes a combination of financial data from a company's existing accounts and non-financial data to assess the likelihood of payment collections under varying conditions. It utilizes an AI model to predict collection outcomes based on historical data related to payment delays and financial hardships. The results help in tailoring collection strategies that are more effective and sensitive to the debtor's circumstances, thereby increasing the probability of recovering dues while maintaining customer relations.