Predict tenant default risk with GPT-4o, Gmail, Slack and collections APIs

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Built by Cheng Siong Chin Cheng Siong Chin
Created on June 05, 2026

Description

How It Works
This workflow automates tenant screening by analyzing payment history, credit, and employment data to predict rental risks. Designed for property managers, landlords, and real estate agencies, it solves the challenge of objectively evaluating tenant reliability and preventing payment defaults.The system runs daily assessments, fetching rent payment history, credit bureau reports, and employment records. An AI agent merges this data, calculates risk scores, and routes alerts based on severity. High-risk tenants trigger immediate email notifications for intervention, medium-risk cases post to Slack for monitoring, while low-risk updates save quietly to databases. Automated collection workflows initiate for high-risk cases.

Setup Steps
Configure payment history, credit bureau, and employment credentials in fetch nodes
Add OpenAI API key for risk analysis and set Gmail/Slack credentials for alerts
Customize risk score thresholds and routing rules in workflow logic

Prerequisites
Payment system API, credit bureau access, employment verification API

Use Cases
Rental application screening, existing tenant monitoring

Customization
Modify risk scoring criteria, adjust alert thresholds

Benefits
Reduces defaults through early detection, eliminates screening bias

Nodes Used (9)

AI Agent
@n8n/n8n-nodes-langchain.agent
Airtable
n8n-nodes-base.airtable
BambooHR
n8n-nodes-base.bambooHr
Gmail
n8n-nodes-base.gmail
HTTP Request
n8n-nodes-base.httpRequest
OpenAI Chat Model
@n8n/n8n-nodes-langchain.lmChatOpenAi
PayPal
n8n-nodes-base.payPal
Slack
n8n-nodes-base.slack
Structured Output Parser
@n8n/n8n-nodes-langchain.outputParserStructured