Easily Manage Your Finances With AI and MCP
How to create an AI-powered personal finance advisor using n8n and Sophtron's banking API.
Managing money is a chore. Nobody enjoys it. Staring at a spreadsheet full of monthly expenses is depressing and manually updating it is a tedious and boring process.
But since I am a developer, I always look for ways to automate the things I hate doing. And with all the recent noise around MCP or Model Context Protocol, I figured it was the perfect time to build something actually useful.
But how exactly does it work and why should you even care using it?
First of all an AI personal finance assistant knows your real-time bank balance to give you accurate and data-driven financial advice. It looks at your transaction history and tells you straight up if you can afford that new gadget or if you need to chill on the coffee runs.
It summarizes your monthly spending on groceries, entertainment, healthcare, and utilities, and warns you when you are spending over the set budget. The model can even detect suspicious transactions to send fraud alerts or spot unnecessary subscriptions to help you control expenses and save more.
For it to work you will need n8n to handle the workflow logic and Sophtron’s API to safely connect to real bank accounts. It might sound a bit technical but I promise it is pretty straightforward once you see how the pieces fit together.
Let me show you how to set it up.
Setting up Sophtron with n8n
Here’s the exact n8n workflow that connects to your credit card or bank account and retrieve transaction data from Sophtron’s banking API on daily basis.
Start by opening your n8n workflow canvas and adding a Schedule Trigger node. You will want to configure this to run at a specific interval, such as once a day. This ensures your finance assistant wakes up automatically to check your latest transactions without you needing to trigger it manually.
Next, you need to pull the actual banking data using an HTTP Request node. In the URL field, enter the Sophtron Banking API’s transaction data endpoint. The most critical part here is the authentication; you need to set up the headers correctly. I recommend visiting docs.sophtron.com (which links to a GitHub repository) to find the specific code or logic required to generate the valid Authorization header.
With the raw data in hand, you need to prepare it for the AI using an Aggregate node. Since the API returns a list of individual transactions, this node consolidates them into a single data structure that the Large Language Model can read and process in one go.
Now for the intelligent part. Connect the aggregated data to an AI model node (like OpenAI). In the prompt or system message, instruct the model to read the transactions provided by Sophtron. You should explicitly tell it to categorize each transaction (e.g., grocery, shopping, utilities) and summarize the spending totals.
This is also where you define your logic — tell the AI to compare the spending against your specific budget limits (for example, “alert me if Amazon spending is over $500”).
Finally, close the loop by adding an email node, such as Gmail. Configure this to trigger only when the AI detects an overage or suspicious activity.
The AI will pass its analysis to this node, which will then send a warning email directly to your inbox, letting you know exactly where you overspent.
In the LLM prompt section, you can put any instruction or question you want to personalize the report. Here are some helpful queries that you can try:
“Look at my credit card transactions for last 3 months, tell me where I am spending my money and help me save more.”
“Find any suspicious transactions or unnecessary subscriptions.”
“What utility bills are due today?”
“Look at my 401k and help me balance my mutual funds.”
“Look at my bills and income and recommend best way to improve my credit score to buy a house in 5 years.”
If you find n8n to be too complicated, you can connect Sophtron with ChatGPT instead. Check out this article for more details.
Also, Sophtron’s MCP server is 100% open-source. You can find the source code in this GitHub page.
What Else Can You Build with Sophtron API?
While n8n is fantastic for automating workflows, the combination of Sophtron’s banking API and the Model Context Protocol opens up a lot more doors. You are effectively giving an LLM safe, read-only access to live financial data, which means you can build standalone agents, CLI tools, or even integrate this directly into your favorite IDE.
Here are a few other ways to use this stack.
A Proactive Bill Manager: Instead of waiting for a due date, create an agent that monitors your variable bills like utilities or credit cards. It can compare the current bill against your historical average and alert you if this month’s electricity usage is weirdly high. It can also ping you a few days before a due date so you never get hit with a late fee again.
Your Personal Wealth Architect: Generic investment advice from ChatGPT is usually useless because it doesn’t know what you actually own. By connecting Sophtron, you can build a retirement planning agent that analyzes your real portfolio distribution. It can track market conditions against your actual holdings and suggest rebalancing strategies or tax-saving moves based on your specific financial situation.
The Financial Health Watchdog: Think of this as your own private credit bureau. You can develop an agent that acts as a 24/7 security guard for your money. It watches for unusual transaction volume or merchant locations that don’t match your habits. It can also track factors affecting your credit score and alert you to potential issues long before they show up on a generic credit report.
Final Thoughts
Let’s address the elephant in the room. Hooking up an LLM to your live bank feed sounds risky because it is. You are piping sensitive financial data through third-party APIs and that requires a level of trust and caution. AI is a powerful tool but you have to be smart about what data you expose and how you secure your keys. It is not a magic solution that excuses you from being careful.
But you can’t ignore the benefits of having it. Since I started using this workflow the biggest change was how effortless it became to understand my cash flow. I didn’t need to analyze spreadsheets to know where my paycheck went. It also built a sense of awareness. Having an agent that gives financial advice and reminders forces you to care about your money in a way that a static banking app never will.
If you are tired of manual tracking give this automation a try. It might just save you some money or at least save you from a bad headache. Let me know what you think about this workflow and if you managed to improve it.
Hi there! Thanks for making it to the end of this post! My name is Jim, and I’m an AI enthusiast passionate about exploring the latest news, guides, and insights in the world of generative AI. If you’ve enjoyed this content and would like to support my work, consider becoming a paid subscriber. Your support means a lot!