Fix Uncategorized Expenses with ChatGPT-Driven Finance Automation

Create a Standardised Classification Schema for ChatGPT

Before you write a single prompt, align on a standardised classification schema that ChatGPT must follow. This includes your chart of accounts, cost centers, project codes, tax codes (if relevant), and allowed expense categories per employee group or country. Put this schema into a concise, machine-readable form (e.g., JSON or a structured table) that can be embedded into prompts or retrieved via an internal API.

Here is an example of how you might present the allowed outputs in a prompt:

Allowed_GL_accounts = [
  {"code": "6100", "name": "Travel - Transport"},
  {"code": "6110", "name": "Travel - Accommodation"},
  {"code": "6120", "name": "Travel - Meals & Entertainment"},
  {"code": "6400", "name": "Software Subscriptions"},
  {"code": "6500", "name": "Office Supplies"}
]

Allowed_cost_centers = ["100-Marketing", "200-Sales", "300-IT", "400-Operations"]

By constraining ChatGPT to this schema, you reduce variance in the output and make it much easier to integrate with your ERP or expense system.

Use Structured Prompts to Infer Category, Cost Center and Policy Violations

For operational reliability, your prompts should ask ChatGPT to return a structured JSON output with specific fields: suggested GL account, cost center, project, policy compliance flag, and a short explanation. This makes it straightforward to parse and feed into your finance systems. Include the raw description, vendor name, amount, date and any existing metadata from your expense tool.

An example classification prompt could look like this:

You are an AI assistant helping a corporate finance team classify employee expenses.

Tasks:
1) Propose the most likely GL account from Allowed_GL_accounts.
2) Suggest the most plausible cost center from Allowed_cost_centers based on description.
3) Check if the expense violates company policy.
4) Explain your reasoning briefly.

Company expense policy (summary):
- Alcohol is not reimbursable.
- First-class travel is not reimbursable.
- Software purchases must go to GL 6400 and cost center 300-IT, unless clearly marketing tools.

Return ONLY valid JSON in this format:
{
  "gl_account": "code from Allowed_GL_accounts",
  "cost_center": "value from Allowed_cost_centers",
  "policy_violation": true/false,
  "violation_reason": "string or empty",
  "explanation": "short explanation for finance team"
}

Allowed_GL_accounts: <insert schema here>
Allowed_cost_centers: <insert schema here>

Expense to classify:
Description: "Uber to client meeting in Berlin"
Vendor: "Uber"
Amount: 32.50 EUR
Country: Germany

This level of structure turns ChatGPT’s output into a reliable building block for automation rather than a free-text suggestion.

Integrate via API into Your Existing Expense or ERP Workflow

To get real value, embed ChatGPT via API directly into the tools your team already uses—whether that’s your expense management system, card platform, or ERP workflow. When a new expense is submitted or imported, your middleware service can call the ChatGPT API with the relevant data, then write back the suggested codes and explanations.

A typical technical sequence looks like this:

1) Expense is created in your expense tool → 2) Webhook triggers a small integration service → 3) Service enriches data (e.g., maps merchant to known vendor categories) and calls ChatGPT with your standard prompt → 4) ChatGPT returns structured JSON with classification and flags → 5) Service writes suggestions and explanation back to the expense record for user or finance review.

This way, reviewers see AI suggestions directly in their normal approval screen, avoiding context switching or manual copy-paste.

Configure Confidence Thresholds and Routing Rules

Not every AI suggestion should be auto-approved. Implement confidence thresholds and routing rules that decide which expenses are auto-posted and which go to manual review. You can estimate confidence from ChatGPT by asking it to return a self-rated confidence score (0–100) or by building a simple heuristic (e.g., whether the explanation contains uncertainty phrases).

Example pattern in your prompt:

Additionally, estimate a confidence score from 0 to 100.
- 90-100: Very clear classification (known vendor, typical amount, matches policy).
- 60-89: Reasonable classification but might need review.
- <60: Unclear classification, must be reviewed.

Add this field to the JSON output as "confidence".

In your integration, you might auto-post expenses with confidence ≥ 90, send 60–89 to fast-lane human review, and route <60 or any policy_violation = true to senior finance review. This keeps risk under control while still delivering high automation rates.

Log All AI Decisions for Auditability and Continuous Improvement

For finance and audit teams, it’s crucial to keep a transparent record of how AI classifications were made. Store each ChatGPT response—including the suggested codes, confidence, and explanation—alongside the final human decision. This enables you to prove to auditors how classifications were derived and to analyse where the AI tends to be corrected.

Regularly export this log to evaluate performance: measure agreement rate between AI and human decisions, typical error patterns, and which policies are most often violated or misinterpreted. Use this feedback to refine prompts, update the allowed schema, or add more examples for tricky categories (e.g., mixed personal/business subscriptions or multi-purpose SaaS tools).

Provide Clear, Human-Readable Explanations to Build Trust

Don’t just return codes—always include a short, human-readable explanation that makes it obvious why ChatGPT chose a specific category. This helps reviewers process entries faster and builds confidence in the system. It also serves as documentation for future audits or internal reviews.

Example explanation style:

"explanation": "Classified as 6100 Travel - Transport because the vendor is Uber, 
amount is typical for a local ride, and the description references a client meeting."

Over time, you’ll notice finance reviewers relying more on these explanations and less on manual inspection, which translates directly into fewer manual touches per transaction and shorter closing cycles.

Implemented carefully, these practices can realistically reduce manual expense classification work by 40–70%, cut days from month-end close, and improve visibility into spend by cost center and project. The exact metrics will depend on your current process maturity and data quality, but even conservative implementations of ChatGPT for expense categorisation typically pay back within a few closing cycles through saved hours and clearer cost control.