Travel claims and receipt checking

Every time an employee submits a travel claim with receipts, the finance team performs a multi-stage review before posting the claim to the ERP system. A team member first checks that receipts are present and complete, then verifies compliance with the internal travel policy. This includes confirming expense categories are allowed, amounts fall within policy limits, and VAT treatment is correct. After the compliance check, a manager typically approves the claim without detailed review, relying on the finance team's first-level checks. Once approved, the claim is manually entered into the ERP system. Throughout this process, the team uses email and spreadsheets to coordinate work and track status. Around ten to fifteen percent of claims do not fit the standard pattern and require judgment calls on policy interpretation, handling of missing receipts, or VAT compliance edge cases.

The process handles roughly three thousand claims per year, taking an average of eleven minutes per claim when everything goes smoothly. The finance team spends significant time on routine data entry and policy checking for straightforward cases, which they describe as the boring part of their work. The external accountant who supports the team has previously integrated with the ERP via its API, confirming that the system is technically open for automation. The organisation has no in-house developer but has allocated budget for external development work to build a solution that fits their needs.

The recommended approach is a hybrid automation combining AI-powered workflow orchestration with mandatory human review gates for exceptions. This design directly addresses the core challenge of this process, which is that eighty-five to ninety percent of claims follow clear, automatable rules while ten to fifteen percent require human judgment. A hybrid system automates the routine work and routes edge cases to the finance team with context and suggested actions, preserving the audit trail and compliance controls while freeing up five hundred fifty hours annually.

The technical foundation for this recommendation rests on three elements that are already in place. First, the ERP has an API and the external accountant has successfully integrated with it, meaning the posting step can be automated reliably. Second, the internal travel policy is clear enough that most compliance checks can be codified as deterministic rules, with only a small subset requiring interpretation. Third, the organisation has confirmed budget for external development, which makes a custom-built workflow solution commercially feasible even without in-house technical capacity. Modern document AI services can handle receipt OCR and data extraction with accuracy high enough to support automated processing when combined with confidence thresholds that route uncertain cases to humans.

The hybrid path scores higher than full end-to-end AI workflow automation because of the compliance requirements and exception frequency. Statutory audit rules and VAT compliance obligations mean every automated posting must be explainable, and the ten to fifteen percent exception rate is too high to ignore. A fully autonomous system would either reject too many claims as exceptions, negating the time savings, or approve edge cases incorrectly, creating compliance risk. The hybrid design places human judgment exactly where it is needed and automates the repetitive work that does not require it. This aligns with the client's stated goal of freeing the team from boring work while keeping them engaged in the judgment calls that matter.

An autonomous AI agent was considered but ruled out because the process does not require adaptive reasoning or complex problem-solving. The policy checks are mostly deterministic, and the financial posting risk makes unsupervised agent decisions inappropriate. RPA could handle some data entry but would struggle with receipt interpretation and the reliance on email and spreadsheets, and would not solve the exception handling problem. A traditional coded solution could work but would require significant custom development of OCR and policy logic that is now available as commodity AI services. The hybrid approach leverages those services while keeping the workflow logic simple and maintainable.

The implementation will require careful work up front to codify the travel policy as explicit rules and build a representative taxonomy of the common exception types. This specification phase is critical because it determines how accurately the system routes claims to the right path. The pilot phase should run in parallel with the manual process for four to six weeks to validate accuracy and tune confidence thresholds before full rollout. If the client invests in this foundational work, the hybrid system should deliver seventy to eighty percent time savings with payback in under two years, while maintaining the audit trail and compliance controls that the manual process currently provides.

The recommended hybrid approach scored 9 out of 10 because it directly addresses the core tension in this process: 85-90% of claims follow clear rules and can be automated end-to-end, but the remaining 10-15% require human judgment for policy interpretation, VAT edge cases, or non-standard situations. By routing exceptions to the finance team while automating routine cases, the hybrid path delivers most of the efficiency gain without the compliance risk of fully autonomous processing. The pure AI Workflow approach scored 6 and remains viable, but it assumes the system can handle all cases without mandatory human gates. Given the statutory audit requirements and the reality that some claims genuinely need judgment calls, pushing for full automation would create audit exposure and likely result in policy breaches that the finance team would catch too late. The hybrid design treats exceptions as a feature, not a failure, and keeps humans in the loop where their expertise actually matters.

RPA and Traditional Code both scored 5 as viable but less attractive options. RPA would struggle with the semi-structured receipt data and the reliance on email and spreadsheets for coordination, making it brittle and high-maintenance. Traditional code could integrate cleanly with the ERP API and handle rule-based policy checks reliably, but building custom OCR and policy interpretation logic from scratch would be expensive and time-consuming compared to using modern document AI services within a workflow framework. The AI Agent path scored only 3 because autonomous agents are over-engineered for this use case. The policy rules are largely deterministic and the compliance requirements demand predictable, explainable decisions, not adaptive reasoning. An agent would add complexity and unpredictability without meaningful benefit.

The implementation starts with policy codification, working directly with the finance team to translate the internal travel policy into explicit rules, decision trees, and thresholds that a machine can execute. This phase also catalogues the common exception types from the 10-15% edge cases, creating a taxonomy that will drive the exception routing logic. The goal is to define which policy checks can be fully automated and which require a human review flag. This foundation is critical because vague requirements lead to either over-sensitive exception routing that floods the team with false positives or under-sensitive logic that auto-approves non-compliant claims.

Next, a proof of concept validates receipt OCR and data extraction using a service like AWS Textract, Google Document AI, or a specialized receipt API such as Taggun or Veryfi. The PoC runs on 50-100 real receipts covering common formats, edge cases, and poor-quality images to establish realistic accuracy expectations. Confidence thresholds are set so that high-confidence extractions proceed automatically while low-confidence or ambiguous receipts are flagged for manual review. This step de-risks the core technical assumption that receipt data can be reliably extracted without constant human correction.

The main build phase develops the workflow orchestration layer, likely using a platform like n8n, Zapier, or a custom Node.js application if the external developer prefers more control. The workflow ingests submitted claims, calls the OCR service to extract receipt data, runs the codified policy checks, calculates totals and VAT amounts, routes exceptions to a human review queue, and posts approved claims to the ERP via its API. Every decision and action is logged with a timestamp, user context, and reason code to satisfy the statutory audit trail requirement. The external developer will integrate directly with the ERP API, leveraging the fact that the accountant has done this before and can provide integration guidance.

A simple human review interface is built for the finance team to process exceptions. This could be a lightweight web app, a shared inbox with structured email templates, or even a spreadsheet-based queue if the team prefers familiar tools. Each exception is presented with the extracted data, the policy rule that triggered the flag, and suggested actions. The reviewer can approve, reject, or override with a reason that gets captured in the audit log. The interface must be fast and low-friction because if reviewing exceptions is cumbersome, the team will lose confidence in the system. Finally, a 4-6 week pilot runs the hybrid system in parallel with the manual process, processing a subset of live claims to measure accuracy, tune thresholds, and build trust before full rollout.