How Finance Teams Are Using AI Agents to Cut Reporting Time by Half (Real Examples)

Finance Teams Are Using AI Agents – What They’re Like To Use. This section outlines the functionality of the finance AI agents available today. It’s helpful to get a sense of how they work and how they can be of use to you and your business. An AI Agent in finance is a piece of software that is used in conjunction with applications such as Stripe, Google Ads and Xero to carry out a task requested of it by a user. As an example, an AI Agent could be used to pull data from Stripe, QuickBooks and Google Ads and use that data to populate a P&L report for a specific time period.

For finance, this is huge. You could tell an AI agent to pull your sales from Stripe, your expenses from QuickBooks and your ad spend from Google Ads and then have it generate a draft P&L report for last month. And then you could go grab a coffee while all of that work gets done for you. This is a huge leap forward from old-school automation.

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Real-World Wins: How Teams Are Slashing Hours

I have seen several companies using AI agents for a long time, and in all cases the biggest benefit was getting back some of their time. They used to spend all of their time on accounting and now they can focus on more interesting things.

Automating the Monthly Close

Automating the monthly close for a company with multiple bank accounts, invoices in an accounting package (such as QuickBooks) and hundreds of transactions per month used to take the first 3 days of the month to reconcile data. The AI agent automates the following: It pulls transaction logs from a user’s bank accounts and matches them against invoices in their accounting package. It then flags any discrepancies that it finds for a human to review. The whole process now takes approximately 2 hours to complete, down from 3 days.

Real-Time Anomaly Detection

A good example of finance automation in practice is variance analysis or expenditure monitoring. A finance team I know have an agent that automates daily tracking and monitoring of variances across different areas of spend such as marketing, and then issues alerts and notification as and when thresholds are passed. The finance manager and the marketing head get a daily alert as well as a summary each month that highlights the areas of spend that have varied the most from target. It is a really effective way to proactively identify and address issues before they become major problems.

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Is It Complicated to Get Started?

Yes, this is all quite complex and expensive. BUT, that is no longer the case. These new tools are becoming increasingly accessible to all finance teams, regardless of size, and you don’t need to be a coding genius to use them.

Picking the Right Platform

Choose a platform that already integrates with all the software your finance team uses today (e.g. QuickBooks, Xero, Salesforce, etc). The “recipes” that most platforms have for the most common finance tasks are all ready to go – so set up a draft report in a few clicks and start seeing the numbers and get a handle on your current P&L.

The Human Is Still in Charge

Note that no matter what technology is used to process the numbers, the human in the middle will always have the final say, whether it is to review the output of the numbers for accuracy, analyze the output to attempt to find the root cause of any issues found in the numbers, or to make strategic decisions based upon the review of the numbers. The role of the human in finance will not be replaced anytime soon, but they will be freed up to focus on more value added work.

A Quick Note on Security

As financial data is highly sensitive, it is of the utmost importance that financial AI agents are secure enough and that the corresponding platform holds the necessary compliance certifications (e.g. SOC 2). Always read reviews and do your research on the security of a financial AI agent before you start using it.

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FAQ

Do I need to know how to code to use AI agents for finance?

As we mentioned before, we are not speaking of programming here. Most of the AI agents are low-code or even no-code and business users can build workflows by adding steps to a process in a drag-and-drop interface or by creating simple text prompts that the AI engine can execute.

Is this stuff expensive?

Costs for this kind of functionality can vary greatly. While some platforms have entry level pricing for small businesses, most offerings are more suitable for larger enterprises. But if you factor in the saved hours, the return on your investment will typically be quite high.

How does an AI agent differ from an automation tool, like Zapier?

Automation tools like Zapier are great for automating simple tasks and creating if this then that workflows. But for more complex, dynamic workflows with multiple steps, the more powerful option is to use an AI agent for finance. In addition to understanding the context of the information, these programs are also able to make simple decisions based off of the data provided to them.

Can these AI agents make mistakes?

Yes, they can go wrong. That is why there is a so called “human-in-the-loop” approach. This means that the AI agent is doing the heavy lifting of work while you are reviewing the output for correctness. This is especially important in the beginning.

How secure is my financial data with these tools?

Reputable providers of AI agent platforms treat financial data security as if it were their own financial data. Look for vendors that clearly describe the security practices of their platform, the type of data encryption that is used, and the various compliance standards to which they adhere (e.g. GDPR, SOC 2).

Can they help with financial forecasting too?

Yes. The AI agent can also be set to pull historical data from your books and, by using trends in that data, run it through a model to create a forecasted budget or sales projection for future time periods. This can serve as a first draft for you and your team to review before making any final decisions.

What are some examples of AI agent tools for finance?

There are a lot of AI agents for finance popping up. Some of the tools and platforms you may hear of are Agentive, Microsoft Copilot for Finance, and many others that are building to integrate AI into existing platforms.

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Conclusion

Using AI for finance work isn’t a feature of some impending futuristic company. It’s already here, and changing at pace. In this post I want to provide some reassurance that you don’t have to let your fear of AI stop you from getting a competitive edge with your finance work.

My advice is to not be intimidated by AI in finance and start small. Choose one boring and repetitive monthly reporting task that you hate to do and see if you can automate it. Chances are you will be surprised by the amount of time you will save.