AI Negotiator Module for Automated Tenant Debt Settlement
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Solution Overview
Problem
Current methods for debt collection in the real estate rental sector are inefficient and costly for landlords, with high losses due to unpaid rent and the need for lengthy legal proceedings, while tenants face negative credit impacts and high fees.
Innovation Solution
An AI-based automatic system for debt settlement that includes a landlord interface, debtor database, and negotiator module to issue settlement proposals, analyze responses, and generate release documents, utilizing generative AI algorithms to optimize communication and negotiation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If landlords use third-party debt collection agencies, then debt collection capability is improved, but collection costs and legal action complexity increase
Solution Approach 1:
The system enables landlords to autonomously manage the entire debt collection process through an automated AI-based platform. The negotiator module automatically communicates with tenants, analyzes responses, proposes settlement terms, and processes payments without requiring third-party collection agencies or complex legal interventions, thereby maintaining collection reliability while reducing operational complexity
Solution Approach 2:
The patent replaces manual mechanical debt collection processes with an automated AI-based electronic system. The negotiator module uses natural language processing and machine learning algorithms to handle tenant communications, analyze financial situations, and propose settlements, substituting the need for third-party collection agencies and reducing the complexity of legal actions required
2Ease of operation
If manual debt collection methods are used, then communication flexibility is improved, but time consumption and productivity loss increase
Solution Approach 1:
The system performs self-service by automatically initiating communications with tenants, analyzing their financial situations and responses, proposing settlement terms, and processing payments without requiring manual intervention from landlords or collection agents, thereby maintaining communication flexibility while dramatically improving collection efficiency and reducing time consumption
Solution Approach 2:
The patent substitutes manual mechanical communication and negotiation processes with an automated AI-based electronic system that uses natural language processing to handle tenant communications, analyzes financial data, and processes settlements automatically, thereby maintaining operational flexibility while eliminating time-consuming manual procedures
3Reliability
If security deposits are used to cover tenant debts, then initial cost protection is improved, but insufficient coverage for full debt amounts occurs
Solution Approach 1:
The system performs preliminary actions by automatically contacting tenants shortly after move-out to initiate debt settlement negotiations before the security deposit expires or before legal actions are required. The negotiator module analyzes tenant financial situations in advance and proposes settlement terms that can be fulfilled using the security deposit or through structured payment plans, thereby maximizing debt recovery while utilizing the existing security deposit mechanism
Data Source
AI summary
Automatic landlord systems, methods and computer program products for settling debtors' debts following termination or end of a property contract are provided. The systems, methods and computer products interface with the landlord to update a debtor's database, collect in a debtor database various data and conditions relating to the debt of each debtor, and negotiate the debt payment by automatically issuing settlement proposals based on the data, receiving debtors' responses and analyzing them to determine agreements and refusals, the former handled by issuing a release document and the latter by further negotiations or by 3rd party collection. The negotiation may be carried out by generative artificial intelligence (GenAI) algorithms implementing multiple agents to represent multiple sides in the process (communication analysis, market analysis, collection, owners, debtors and negotiations strategies) and thereby optimize the debt collection process.