Agentic AI in finance speeds up operational automation

Introduction

In the financial sector, automation powered by AI is transforming how companies operate. This tutorial will teach you how to create a simple agentic AI system that can help automate basic financial tasks. We'll build a system that can analyze financial data and make basic recommendations, laying the foundation for more complex automation. This beginner-friendly approach will use Python and basic AI concepts to demonstrate how financial operations can be streamlined through intelligent automation.

Prerequisites

Before starting this tutorial, you'll need:

• A computer with Python 3.7 or higher installed
• Basic understanding of Python programming concepts
• Internet access to install packages
• Familiarity with financial concepts like budgeting and expense tracking

Why these prerequisites? Python is the most accessible programming language for beginners to start working with AI. Understanding basic financial concepts will help you grasp how our AI agent will make decisions. We'll use simple libraries that don't require advanced technical knowledge.

Step-by-Step Instructions

1. Set Up Your Development Environment

First, we need to create a working space for our AI agent. Open your terminal or command prompt and create a new folder for this project:

mkdir financial_agent
 cd financial_agent

Next, create a Python file called financial_agent.py and open it in your code editor. This will be our main file where we'll build the AI agent.

2. Install Required Libraries

Our AI agent will need some basic tools to work with financial data. Install the following packages:

pip install pandas numpy

Why we install these packages: Pandas helps us manage and analyze financial data, while NumPy provides mathematical functions needed for data processing. These are essential for any data-driven AI system.

3. Create Basic Data Structure

Let's start by creating some sample financial data that our AI agent can work with:

import pandas as pd

data = {
    'month': ['January', 'February', 'March', 'April', 'May'],
    'income': [5000, 5200, 4800, 5500, 5300],
    'expenses': [3000, 3200, 2800, 3500, 3300],
    'savings': [2000, 2000, 2000, 2000, 2000]
}

df = pd.DataFrame(data)
print(df)

This creates a simple dataset with monthly income, expenses, and savings data. Our AI agent will analyze this data to make recommendations.

4. Build the AI Agent Framework

Now we'll create a basic AI agent class that can analyze our financial data:

class FinancialAgent:
    def __init__(self, data):
        self.data = data
        
    def analyze_finances(self):
        # Calculate total income and expenses
        total_income = self.data['income'].sum()
        total_expenses = self.data['expenses'].sum()
        
        # Calculate average monthly savings
        avg_savings = self.data['savings'].mean()
        
        print(f"Total Income: ${total_income}")
        print(f"Total Expenses: ${total_expenses}")
        print(f"Average Monthly Savings: ${avg_savings}")
        
        return total_income, total_expenses, avg_savings

This agent will analyze the basic financial metrics. The framework is simple but can be expanded to include more complex analysis.

5. Add Recommendation System

Let's enhance our agent with simple recommendation capabilities:

    def make_recommendations(self):
        # Analyze if savings are consistent
        if self.data['savings'].std() < 100:  # If savings variation is low
            print("\nRecommendation: Your savings are consistent. Consider investing in low-risk options.")
        else:
            print("\nRecommendation: Your savings vary significantly. Consider setting up automatic savings.")
        
        # Analyze spending trends
        if self.data['expenses'].mean() > self.data['income'].mean() * 0.7:
            print("Recommendation: Your expenses are high. Consider budgeting strategies.")
        else:
            print("Recommendation: Your expenses are within a healthy range.")

These recommendations are based on simple financial rules that an AI agent can understand. The agent makes decisions based on patterns in the data.

6. Run the Agent

Now let's put everything together and run our AI agent:

# Create the agent
agent = FinancialAgent(df)

# Analyze the data
income, expenses, savings = agent.analyze_finances()

# Make recommendations
agent.make_recommendations()

This will run our agent and show you the financial analysis and recommendations based on the sample data.

7. Extend with Simple Automation

Let's add one more feature - automated monthly reporting:

    def generate_monthly_report(self):
        print("\n--- Monthly Financial Report ---")
        for index, row in self.data.iterrows():
            print(f"{row['month']}: Income ${row['income']}, Expenses ${row['expenses']}, Savings ${row['savings']}")
        
        print("\n--- Summary ---")
        print(f"Total Income: ${self.data['income'].sum()}")
        print(f"Total Expenses: ${self.data['expenses'].sum()}")
        print(f"Net Savings: ${self.data['savings'].sum()}")

This feature automates the creation of financial reports, which is a common task in financial operations that AI can help with.

8. Complete the Main Execution

Update your main execution code to include the new reporting feature:

# Generate full report
agent.generate_monthly_report()

This completes our basic AI agent that can analyze financial data and provide automated recommendations. The agent is simple but demonstrates how AI can be used to automate financial tasks.

Summary

In this tutorial, we've built a basic financial AI agent that can analyze data and provide automated recommendations. While this is a simple example, it demonstrates the foundation of how agentic AI works in financial operations. The agent can:

• Process financial data
• Identify trends and patterns
• Make simple recommendations
• Automate reporting tasks

This approach mirrors what companies like SEI are doing with AI - using data-centric systems to drive automation. As you continue learning, you can expand this agent to include more complex analysis, integrate with real financial APIs, or add machine learning capabilities for better decision-making.

Remember, this is just the beginning. Real-world financial AI systems are much more complex, but understanding these fundamentals is crucial for building more advanced automation solutions.