Agentic AI for Finance — AI + Finance Automation with n8n

Course Title:

Agentic AI for Finance — AI + Finance Automation with n8n

Course Overview

This course represents the final and most advanced stage of your AI-in-Finance learning journey.
It enables finance professionals to build, automate, and deploy intelligent finance agents using n8n, OpenAI, and Tally data integration.

Through this program, learners gain the ability to design end-to-end finance automation systems — from data input and analysis to reporting, commentary, and forecasting — all executed by AI agents with near-zero manual intervention.
The course concludes with a production-grade deployment and certification as a Certified AI Finance Automation Practitioner (CFAP).

Must Complete Before Starting

Before beginning this course, learners must complete all the following foundation and intermediate programs to ensure readiness for agentic-level automation:

1. Basics of Tally – Software Orientation & Key Concepts

2. Basics of Finance – Foundation Course

3. Basics of AI – Foundation Course

4. Foundations of AI Automation for Accounting Agents

5. AI in Finance — FP&A & Treasury (Tally Integration)

These courses ensure familiarity with finance concepts, Tally data structure, automation principles, and applied AI logic.

Complete Course Curriculum

Module 1: Foundations of AI, Agents & n8n Ecosystem (Finance Edition)

Duration: 45 minutes
Objective: Understand how AI, automation, and agent-based workflows integrate in financial systems.

Key Topics:

• Generative AI, LLMs, and agent architecture

• No-code vs low-code in financial automation

• n8n UI and workflow design principles

• How ChatGPT evolves into a Finance Intelligence Agent

Project:
Demo Workflow — “From Chat Input to Financial Summary”
Learners input monthly P&L data, and the AI automatically generates financial highlights and ratio summaries.

Outcome:
Learners understand how to structure AI agents and visualize n8n as the foundation for intelligent finance systems.

Module 2: Setup, APIs & Webhook Triggers

Duration: 1 hour
Objective: Configure n8n to connect various data sources and automate finance workflows.

Key Topics:

• Installing n8n (local and cloud environments)

• Connecting Google Sheets, Excel, and OpenAI

• Webhook creation and testing

• JSON parsing for finance data

Project:
Webhook → OpenAI → Gmail:
Build an AI workflow that reads uploaded financial data, interprets it, and automatically emails the summarized insights.

Outcome:
Learners can connect APIs, handle structured data, and build foundational automation logic for finance operations.

Module 3: Workflow Design Without AI (Finance Context)

Duration: 1 hour
Objective: Learn workflow logic before adding AI components.

Key Topics:

• IF, Merge, Split, and Loop nodes

• Data validation, numeric thresholds, and conditional routing

• Logging and exporting finance data to Sheets

• Building MIS routing logic

Project:
Finance Data Router:
Creates a workflow that routes monthly MIS reports to specific departments using data-based conditions.

Outcome:
Learners design rule-based automations — establishing the control logic layer for future AI workflows.

Module 4: Adding Intelligence with OpenAI

Duration: 1 hour
Objective: Integrate OpenAI into n8n to analyze financial reports and generate narratives.

Key Topics:

• Setting up the OpenAI node in n8n

• Writing financial prompts for structured analysis

• Handling JSON responses and extracting data

• Defining the “Finance Analyst Agent” role

Project:
AI Financial Analyzer:
Reads a P&L report and automatically produces a professional financial summary with profitability insights.

Outcome:
Learners can embed AI text analysis into workflows and transform raw data into analytical commentary.

Module 5: Prompt Design & Role-Based Agents

Duration: 1 hour
Objective: Develop multi-role AI agents capable of analyzing, verifying, and summarizing financial data.

Key Topics:

• Role-based prompting (Analyst → Reviewer → CFO)

• Multi-agent collaboration

• Root-cause analysis via prompt chaining

• Verification and iterative refinement

Project:
Budget Variance & Root-Cause Agent:
Compares Budget vs Actual data, identifies deviations, and explains the corrective actions.

Outcome:
Learners build collaborative AI systems that mimic real-world financial reporting workflows.

Module 6: Context & Real Data Integration

Duration: 1.5 hours
Objective: Connect and clean multi-period financial datasets for AI-based analysis.

Key Topics:

• Importing historical finance data

• Cleaning and normalizing data (duplicates, missing values)

• Enriching with external variables (interest rates, inflation, etc.)

• AI-based anomaly and trend detection

Project:
Anomaly & Insight Agent:
Analyzes several months of P&L data to detect unusual patterns and generate executive insights.

Outcome:
Learners manage real data workflows, improving the context and depth of AI-generated insights.

Module 7: Finance Agents 1 & 2 – Forecasting + Cashflow Intelligence

Duration: 2 hours
Objective: Automate forecasting and liquidity management through AI and automation.

Projects:
1️⃣ Forecasting & Scenario Simulation Agent – Predicts next quarter’s revenue and expenses while running “What-if” models.
2️⃣ Cashflow & Working Capital Intelligence Agent – Tracks inflows/outflows, detects liquidity gaps, and suggests remedies.

Includes Quiz 1: Forecasting & Liquidity Logic Check

Outcome:
Learners can build AI systems that predict future trends, manage liquidity, and simulate different financial scenarios.

Module 8: Finance Agents 3 & 4 – Variance & KPI Narration

Duration: 1.5 hours
Objective: Build agents for executive-level communication and performance storytelling.

Projects:
1️⃣ Budget Variance & Root-Cause Agent – Explains deviations and patterns.
2️⃣ Management KPI Narrator Agent – Reads key metrics and generates ready-to-send executive summaries.

Key Concepts:

• Dynamic context prompting

• Executive summary generation

• Slide and email automation

Outcome:
Learners automate the creation of management commentary and MIS narratives with minimal manual effort.

Module 9: Finance Agent 5 – Financial Insight & Anomaly Detection

Duration: 1 hour
Objective: Create AI agents for automatic analytical review of financial statements.

Key Topics:

• Ratio-based anomaly detection

• Multi-period comparisons and financial health scoring

• AI-based recommendations for corrective actions

Project:
Insight Agent:
Reads multiple financial statements, identifies issues, and recommends targeted responses.

Outcome:
Learners automate analytical reviews and build intelligent anomaly detection models.

Module 10: Workflow Reliability & Error Handling

Duration: 1 hour
Objective: Build fault-tolerant and self-correcting finance automations.

Key Topics:

• Numeric validation and sanity checks

• Schema and threshold verification

• Retry logic with alerts and logging

• Automated email notifications for errors

Project:
Error-Proof Forecast Agent:
Validates forecasts, retries failed executions, and sends detailed error summaries.

Outcome:
Learners develop reliable, self-monitoring workflows that ensure no silent failures.

Module 11: Deployment, Testing & Capstone Project

Duration: 1.5 hours
Objective: Deploy live finance automation systems with secure governance.

Key Topics:

• Hosting on n8n Cloud or VPS

• Secure environment variables and credentials

• Version control and rollback strategies

• Monitoring, notifications, and QA testing

Capstone Project:
AI Finance Automation Suite
Integrates all 5 Finance Agents (Forecasting, Cashflow, Variance, KPI Narration, and Anomaly Detection) into a single workflow.

Deliverables:

• Workflow JSON exports

• Dashboards and reports

• Reflection quiz and validation test

Certification Outcome:
🎓 Certified AI Finance Automation Practitioner (CFAP)

Outcome:
Learners graduate as advanced finance automation professionals, capable of building intelligent, production-grade AI systems for analysis, forecasting, and reporting.