🔬 Generate monthly AI financial reports with OpenAI and email/Slack distribution

⚡ 19 views · 🔬 Document Extraction & Analysis

Description

This n8n workflow automatically fetches monthly financial statements, normalizes the data, performs KPI calculations and trend analysis, detects anomalies, generates AI-powered executive insights and recommendations, creates professional reports, and distributes them to stakeholders while maintaining historical records.

Key Insights

• Consistent data formats from accounting systems (QuickBooks, Xero, etc.) are critical for reliable normalization and analysis.
• AI-generated insights are only as good as the input data quality — always validate fetched statements and mappings first.
• Monthly scheduling ensures timely reporting; consider adding manual triggers for ad-hoc runs during testing.

Workflow Process

1. Initiate the workflow with the Monthly Schedule Trigger node (runs on the 1st of each month at 8 AM).
2. Fetch current period financial statements (P&L, balance sheet, cash flow) using the accounting API nodes.
3. Fetch previous period data for accurate YoY/MoM comparisons.
4. Merge all statements, normalize formats, validate integrity, and calculate standardized KPIs/metrics using data transformation nodes.
5. Analyze trends, variances, and detect anomalies/unusual patterns.
6. Send cleaned financial data to an AI model (OpenAI/Claude) to generate natural language executive summaries, key insights, and actionable management recommendations.
7. Format and generate professional HTML/PDF reports (with charts/visualizations if configured).
8. Store the report and metrics in a database for historical tracking, post summary to Slack, and email the full report to management/stakeholders.

Usage Guide

• Import the workflow into n8n and configure credentials for your accounting system(s), AI provider, database, Slack webhook, and email (SMTP).
• Map fields correctly in the normalization/validation nodes to match your source data structure.
• Test end-to-end with sample/historical financial data before enabling the schedule.
• Execute manually first via the Execute workflow button to verify each step (especially API fetches and AI output quality).

Prerequisites

• API access to accounting system (QuickBooks, Xero, SAP, or direct database connection)
• OpenAI or Claude API key for insight generation
• Database (PostgreSQL, MySQL, etc.) with tables ready for reports and metrics storage
• SMTP server or email service credentials
• Slack webhook (optional for notifications)
• PDF/HTML generation tools (if using external services like WeasyPrint via code node or Make.com-style nodes)

Customization Options

• Adjust the schedule (e.g., quarterly instead of monthly) in the trigger node.
• Modify the AI prompt in the LLM node to change the style/tone of summaries or add specific focus areas (e.g., cost control, revenue growth).
• Extend anomaly detection rules or add more KPIs in the analysis section.
• Change distribution channels (add Teams, Discord, Google Drive upload, etc.) by modifying or adding nodes after report generation.
• Add conditional branching for high-priority alerts (e.g., if major anomaly detected → immediate notification).

🔗 Nodes Used

Send Email, HTTP Request, Postgres, Schedule Trigger

📥 Import

Download workflow.json and import into n8n: Workflow menu → Import from File

📖 Importing guide · 🔑 Credential setup