Stop Out-of-Policy Expense Claims with ChatGPT Automation

Build a Custom GPT Trained on Your Travel & Expense Policy

Instead of using the generic ChatGPT interface, create a custom GPT for expense policy enforcement that has your current T&E policy, country addenda and approval rules embedded as its knowledge base. Structure the policy into sections (e.g. flights, hotels, meals, ground transport, per diems, gifts) and explicitly define thresholds, exceptions and approval hierarchies.

In the GPT configuration, define a stable system prompt that tells the model how to reason, what to output and when to flag items as non-compliant or borderline. For example:

System instructions example:
You are an AI assistant for the Finance department.
You review employee expense claims for policy compliance.
You receive:
- The expense line item (category, amount, currency, date)
- The employee's free-text description
- Context (employee role, cost center, project, location)
- Relevant excerpts from the travel & expense policy

Your tasks:
1) Decide if the expense is In-Policy, Borderline, or Out-of-Policy.
2) Explain your reasoning in 2-4 sentences referencing concrete policy rules.
3) If Out-of-Policy or Borderline, suggest a corrective action (e.g. partial reimbursement, manager approval).
4) Provide a separate, employee-friendly explanation without legal jargon.

Test this custom GPT with historic expense data and refine the wording until controllers trust its classification and explanations.

Design a Standard Input Schema for Each Expense Line

To get consistent results, standardise what you send from your expense tool or ERP to ChatGPT. Map fields like employee ID, role, department, project code, merchant, category, amount, currency, date, city/country, cost center and raw description into a simple JSON or tabular format.

When using ChatGPT via API or an internal tool, wrap each line item in a consistent prompt structure. For example:

Example prompt to the custom GPT:
Review the following expense for policy compliance.

Employee role: Senior Consultant
Department: Sales
Cost center: 1203
Project: Client XYZ implementation
Location: Paris, France

Expense category: Hotel
Amount: 280
Currency: EUR
Date: 2025-03-14
Description (employee): "Two nights at Hotel Central near client office"

Relevant policy excerpt:
- Maximum hotel rate in Paris: 220 EUR per night (incl. taxes).
- Exceptions require prior written approval from the project lead.

Output as JSON with fields: decision, reasoning, corrective_action, employee_message.

This structure makes it easier to parse ChatGPT's output back into your expense system, apply workflows and generate consistent audit trails.

Integrate ChatGPT Checks into the Expense Submission Workflow

For real impact, embed AI-based expense checks where employees and approvers already work. Use your IT team or a partner like Reruption to connect your expense management system (or card transaction feed) to ChatGPT via API. Trigger a policy check when an expense is submitted, when a report is completed, or before a manager approves.

Configure the integration so that low-risk, clearly in-policy items are auto-approved, borderline cases are highlighted to managers with the model's reasoning, and clear out-of-policy items require finance review. Present the AI's employee-friendly explanation directly in the UI so submitters see immediately why something is flagged and what to change (e.g. recategorise, split business/private, add missing approval).

Create Playbooks for Frequent Policy Violations

Use ChatGPT's reasoning not only to detect issues but also to drive consistent resolutions. Analyse a few months of expense checks to identify recurring patterns of non-compliance: for example late-booked flights above fare caps, hotel upgrades beyond limit, minibar charges, or recurring SaaS subscriptions paid via personal card.

For each pattern, define a playbook: whether to reimburse partially, require written manager justification, or decline; what alternative behaviour you want to nudge; and what wording is acceptable in employee communication. Then codify these into the custom GPT's instructions so it can propose the correct corrective action and messaging automatically. Example snippet:

Policy playbook instruction:
If the only violation is the nightly hotel rate exceeding the city cap by <= 15%,
AND the trip was for a client project,
THEN recommend partial reimbursement up to the cap
AND suggest that future bookings should be made at least 7 days in advance
OR using preferred hotels when available.

This keeps decisions predictable and reduces back-and-forth between finance and employees.

Generate Tailored Explanations for Employees and Auditors

One of ChatGPT’s strengths is adapting the same underlying reasoning to different audiences. Use this to your advantage by asking the model to produce two types of explanation for every flagged expense: a short, clear, non-legal explanation for the employee, and a more detailed one referencing specific policy clauses for controllers and auditors.

Extend your prompts accordingly:

Extend system prompt:
For every decision, create:
- employee_message: A concise explanation (max 120 words) in neutral, respectful tone.
  Avoid legal terminology. Explain what can and cannot be reimbursed and why.
- audit_note: A detailed note (max 250 words) referencing the exact policy section
  and numerical thresholds that justify the decision.

Store audit notes with the expense record in your finance system. This saves time in audits and makes contested decisions much easier to defend.

Monitor Performance and Calibrate Thresholds with Real Metrics

Once your AI expense review process is live, treat it like any other control and measure its performance. Track key metrics: percentage of expenses checked by AI, share auto-approved vs. escalated, number and value of out-of-policy claims detected, false positive rate, time saved for controllers and approvers, and employee dispute rates.

Review a sample of AI decisions monthly with finance and HR, adjust prompts, add or refine policy excerpts, and change thresholds for auto-approval as trust grows. Over time you should see a reduction in non-compliant spend in targeted categories (often 10–25%), fewer manual line-by-line checks, and faster close of expense reports without increasing conflict.

Implemented pragmatically, these practices can turn ChatGPT into a scalable control layer that reduces out-of-policy spend, cuts manual review time by 30–50%, and improves transparency for both employees and auditors—without forcing you to replace your existing expense tools.