How Finance Teams Use ChatGPT to Improve Financial Analysis and Operations

Artificial intelligence is quickly becoming part of how finance teams get work done. Large language models (LLMs) like ChatGPT and Claude are now used across everything from content creation to strategic planning, but one of their most practical applications is financial analysis. Tasks that used to require digging through spreadsheets, building models, and stitching together data from multiple sources can now be done in minutes with just a bit of prompt engineering.

In this guide, we’ll walk through how finance teams can use ChatGPT to improve analysis and streamline workflows. We’ll cover the difference between using an LLM for research versus using an agent for operations, key use cases for AI in finance, and important limitations to keep in mind. By the end, you’ll have a clear understanding of how to incorporate AI into your financial workflows effectively.

If you’re already familiar with AI tools, you may be looking for a more integrated solution. Slash is an AI-native financial platform that allows you to connect your preferred AI agent directly to your financial stack through MCP. This setup lets you manage finances through simple prompts instead of manual workflows. With support for ChatGPT-compatible agents, you can send payments, manage cards, analyze invoices, and review cash flow from your Slash account.¹ Continue reading to learn more.

Corporate cards built for control

Cashback, automation, and insights, simplified.

Get Started

Understanding LLMs vs. AI Agents in Financial Workflows

When using artificial intelligence in finance, or any task, it helps to understand the difference between LLMs and agents.

You’re likely already familiar with LLMs. These are chatbots that you prompt to get text-based responses. When you ask a question, the model generates an answer based on its training data, the context you provide, and information it can retrieve from the web. LLMs are best suited for analysis, research, summarization, and drafting. They can review a P&L, explain trends, or turn raw numbers into a clear narrative, but they do not take action on your behalf.

Agents are the operational counterpart. Instead of just generating text, an AI agent can take actions across systems like financial platforms, internal tools, or other API-enabled software. Rather than clicking through a dashboard like a human, agents interact with systems through APIs, which allow software to communicate directly with each other. APIs tell the agent what actions are possible, such as creating a payment or retrieving transaction data, and what inputs are required to complete them.

The simplest way to think about it is this: LLMs help you understand what to do, and agents help you actually do it.

In a financial workflow, the LLMs and agents can work together. A team might use an LLM chatbot to analyze reports, identify spending trends, or draft a budget. From there, an agent can take the next step by acting on those insights, whether that means sending payments, creating new invoices, or updating spend controls in connected systems.

How to Use ChatGPT for Finance

Without agentic capabilities, you may be only using a small part of what ChatGPT can do. It can handle much more than answering questions; behind the scenes, ChatGPT can be connected to external systems like Slash through what OpenAI calls “tool calling.” Connecting it to your business's financial stack through an agent setup allows the analysis to flow directly into financial operations. Here are some possible use cases:

Financial analysis and reporting

You can paste in a P&L, balance sheet, or transaction export and have ChatGPT summarize your performance, explain variances, and highlight key trends. It can be especially useful for turning dense data into executive-ready narratives or quickly identifying what changed and why. This is primarily an LLM use case, since it focuses on reasoning and explanation. An agent can extend this by pulling reports or distributing outputs, but the core work is analytical.

Budgeting and forecasting

Teams can use ChatGPT to build rolling forecasts, identify key drivers of cash flow, and explore how changes in revenue or costs may impact future performance. This is largely handled by the LLM, since it relies on reasoning and scenario generation. An agent becomes useful when you want to connect insights to live data or push updates into your financial planning tools.

Research and market analysis

ChatGPT can compare companies, break down pricing models, and understand trends that could impact your business’s financial performance. With web access enabled, it can synthesize insights across multiple current sources much faster than researching yourself. This is primarily an LLM task, since it centers on gathering and interpreting information, with agents optionally pulling in structured data from connected sources.

Automating documentation

ChatGPT is effective at turning internal data or meeting notes into something readable. Whether it’s a monthly update, a board summary, or process documentation, it can take rough inputs and shape them into a clear, consistent narrative. Most teams use it to generate a strong first draft, then refine tone and details as needed. This is primarily an LLM use case.

Payments and operational workflows

Once connected to your financial systems, an agent can take structured inputs and carry out actions like sending payments, creating invoices, or updating records.

Financial controls and policy enforcement

ChatGPT can help define approval workflows, spending limits, and internal policies by turning high-level rules into clear guidelines. When paired with an agent, those policies can be enforced within your financial systems, applying controls to transactions and approvals just by prompting.

The standard in finance

Slash goes above with better controls, better rewards, and better support for your business.

Get Started

Key Benefits and Limitations of Using ChatGPT in Finance

The advantages of using AI in financial workflows are significant, but it’s not something you want to run unchecked. ChatGPT can dramatically improve speed and efficiency, but it still requires oversight and the right systems around it. Here are some considerations to bear in mind:

Benefits of using ChatGPT in finance

Saves time on manual work: Tasks like summarizing reports, drafting updates, and analyzing data that used to take hours can be done in minutes.
Makes financial data easier to understand: ChatGPT can translate complex numbers into clear explanations, helping teams communicate insights across the business.
Speeds up decision-making: It helps teams get to a strong first answer quickly, so they can focus more on validation and execution.
Improves access to insights: Non-finance stakeholders can interact with financial data more easily, reducing confusion around reporting and analysis.

Limitations of using ChatGPT in finance

Outputs can be inaccurate or incomplete: ChatGPT can misinterpret data or generate confident but incorrect answers, especially without proper context.
Data privacy and security considerations: Sensitive financial information should be handled carefully, especially when using non-enterprise AI tools.
Contextual gaps: ChatGPT may not always understand your full financial stack, so outputs may miss important context for executing certain unless you provide it.

From Insight to Execution: Combining ChatGPT with Financial Tools

Turning AI insights into real financial actions requires connecting ChatGPT to your systems through an agent setup. Though setup and tooling differs between platforms, here’s a general overview for connecting an OpenAI agent with your financial stack:

Step 1: Define which systems and actions to connect

Connecting an agent to your stack starts by deciding which financial systems the agent can access and what actions it should be able to take. This could include things like retrieving account data, initiating payments, issuing cards, or updating records. Each of these actions needs to be clearly defined to the agent during setup so it knows what is possible and what inputs are required.

Step 2: Expose actions as tools

Once the actions are identified, they are defined as tools that the model can use. In OpenAI’s ecosystem, this is done through tool calling, where each tool represents a specific capability with a clear description and structured inputs. These tools act as the bridge between ChatGPT and the external financial platform.

Step 3: Set permissions and guardrails

Before the agent is put into use, you should define what it is allowed to do and under what conditions. This can include approval workflows, spending limits, or restrictions on certain actions. These controls ensure that even though the interface is conversational, the underlying financial operations remain secure and compliant.

Step 4: Connect the tools to the agent

The tools are then passed into the model as part of the application setup, usually through the OpenAI API. Each tool is defined with a name, description, and required inputs, and included in the request so the model knows what capabilities are available. When a user sends a message, the model evaluates whether it can answer directly or if one of those tools should be used.

Step 5: Execute the action and return the result

After the model selects a tool, it generates a structured request with the required inputs. That request is sent to the connected financial system, which performs the actual action, such as creating a payment or retrieving data. The system then returns the result, and the model translates it into a clear, readable response for the user. This keeps the experience conversational, even though the execution is happening through underlying systems with their own permissions and controls.

How Slash Connects ChatGPT to Your Financial Operations

Slash supports two ways of connecting ChatGPT to your account:

MCP (Model Context Protocol): Add Slash's MCP server as a connection in your ChatGPT client. You'll need an API key from your Slash dashboard.
Custom GPT Actions: Build a Custom GPT and import the Slash Public API spec under Actions, then authenticate with your API key.

Once connected, ChatGPT can check balances, search transactions, create and manage virtual cards, set spending limits, move money between accounts, create invoices, and monitor your business spending entirely through conversation. Here’s what you can control in your Slash dashboard using an integrated AI agent:

Dynamic business banking: Open unlimited virtual accounts to separate operational funds to give teams clearer visibility into cash flow. Manage multiple business entities from a single dashboard, with consolidated reporting and clear visibility across accounts.
Accounting & ERP integrations: Sync transaction data with QuickBooks Online, Xero, or Sage Intacct to streamline reconciliation, reporting, and month-end close.
Slash Visa® Platinum Card: A corporate charge card that earns up to 2% cash back on company spending, with configurable spending rules, card controls, and encryption-grade fraud protection.
Diverse payment methods: Slash supports a wide range of payments, including card spend, global ACH, international wire transfers to over 180 countries via SWIFT, and real-time domestic payments through RTP and FedNow.
Native cryptocurrency support: Convert funds into USD-pegged stablecoins such as USDT or USDC to send transfers on the blockchain, offering an alternative payment method that can reduce costs and settlement times.⁴

Apply in less than 10 minutes today

Join the 5,000+ businesses already using Slash.

Frequently Asked Questions

What is the best AI tool for finance?

The best AI tool depends on your specific needs, such as financial analysis, reporting, expense management, or payments. Many teams use a combination of tools, including LLMs like ChatGPT for analysis and communication, along with financial platforms or automation tools for execution and control.

Can ChatGPT provide insights for managing corporate credit cards or vendor payments?

Yes, ChatGPT can help analyze spending patterns, summarize transactions, and suggest ways to optimize vendor payments or card usage. It cannot directly manage accounts or execute payments without being connected to a financial system or agent.

Can finance teams use ChatGPT to simulate financial scenarios for strategic planning?

Yes, ChatGPT can help model different financial scenarios. It’s useful for exploring best- and worst-case situations using your data inputs, but results should be validated with real data analysis and financial models before making major decisions.