Travel claims and receipt checking

The Travel Claims and Receipt Checking process begins when an employee submits a travel claim along with supporting receipts. A member of the three-person finance team then picks up the claim, manually checks each receipt against the company's internal travel policy, identifies any breaches or anomalies, and passes the claim to a manager for approval. The manager either approves the claim or escalates exceptions before the approved claim is posted to the ERP system. The process runs multiple times per day, totalling around 3,000 claims per year, and currently takes an average of eleven minutes per claim, which adds up to roughly 550 hours of manual effort annually.

The process sits at the intersection of unstructured input and structured rules. Receipts arrive as images and PDFs, which cannot be handled by simple rule-based logic alone, but the underlying policy and the ERP data model are well-defined. The finance team also works with an external accountant who has previously integrated with the ERP API, which means the integration groundwork is already partially laid. Compliance requirements are significant: a statutory audit trail is mandatory, VAT rules must be applied correctly, and all documents must be retained for seven years. Every automated posting must be explainable to an auditor, which shapes the architecture in important ways.

The end goal is a validated claim that either passes straight through to ERP posting or is surfaced to a human reviewer with enough context to make a fast, informed decision. The client's stated ambition is to remove the finance team from the routine majority of claims while keeping them firmly in control of the cases that genuinely need their judgement. That framing maps very cleanly onto the recommended automation approach.

The Hybrid path earns its recommendation here because the process has two clearly distinct populations of claims: a routine majority that follows predictable rules, and a meaningful exception minority that genuinely requires human expertise. A system that tries to automate everything ignores the real compliance risk at the edges. A system that leaves everything manual ignores an obvious and well-quantified efficiency opportunity. The Hybrid splits those two populations cleanly, routing the 85 to 90 percent of straightforward claims through automated extraction, policy checking, and ERP posting, while sending the remaining 10 to 15 percent to a human queue with the relevant context already assembled. That is precisely what the client asked for.

The compliance requirements are the most important factor in ruling out a fully automated AI Workflow or an autonomous AI Agent. Statutory audit trail obligations, correct VAT treatment, and seven-year document retention are not optional controls that can be layered on later. They must be designed into the architecture from day one, and they require that every automated decision be logged with the rule applied, the data used, and the timestamp. An autonomous agent making end-to-end decisions would be very difficult to make explainable to a statutory auditor, and the finance team does not have the in-house capacity to supervise and correct agent behaviour in production. The Hybrid avoids both problems: automated decisions are deterministic and logged, and ambiguous cases never post without a human sign-off.

The client has no stated tech stack preference, which is actually an advantage here. It means the external developer can choose the workflow platform that fits the ERP API cleanly rather than being forced into a particular tool. Platforms such as Make, n8n, or a lightweight Python service are all reasonable candidates, and the choice should be driven by what the external developer knows well and what will be easiest for the finance team to understand and monitor over time. The AI document-processing layer for receipt extraction is the one component where the choice matters most: OCR plus a large language model extraction service will handle the variety of receipt formats far better than any purely rules-based approach. That layer feeds structured data into the policy check logic, which then makes a deterministic pass, fail, or escalate decision.

Comparing the Hybrid against the other viable paths makes the case clearer. Traditional Code could handle the structured policy checks and ERP posting, but it cannot read unstructured receipt images without an AI extraction layer sitting in front of it, and maintaining a pure rulebook as policy changes adds ongoing developer cost with no upside. RPA is the wrong tool entirely here: the ERP already has an API, so RPA's main justification, which is automating screen interactions where no API exists, does not apply, and the maintenance burden for a team without in-house developers is a genuine operational risk. An AI Agent scores lowest among the automation paths because the compliance and VAT requirements demand deterministic, auditable logic rather than the kind of emergent decision-making that agents are built for.

The honest tradeoff with the Hybrid is that it requires more upfront design discipline than a simpler solution. The policy rules must be fully codified before the build starts, the exception routing interface must be intuitive enough for a busy finance team to use without training, and the audit log must be treated as a first-class deliverable rather than an afterthought. None of those things are technically hard, but all of them require careful attention during the discovery and build phases. The implementation roadmap is structured to address each of these risks in sequence, starting with policy codification before any code is written, and ending with a parallel run that validates outputs against the existing manual process before full cutover.

The Hybrid path scores nine out of ten and is the clear winner because this process has a natural split between a well-defined routine majority and a smaller but genuinely complex exception minority. The routine cases, roughly 85 to 90 percent of the 3,000 annual claims, follow consistent policy rules against structured ERP data, and the only reason they consume so much time today is that a human being picks up every single one regardless of complexity. Hybrid automation respects that reality: it removes people from the repetitive checking while keeping them firmly in control of the cases that actually need their judgement. The compliance picture reinforces this choice, because every automated decision can be logged with the rule applied, the data used, and the timestamp, giving auditors a complete and explainable trail without requiring a human to have touched every posting.

The AI Workflow path, scored at six, is viable but not the right fit here. A fully automated workflow could handle the routine cases just as well as the Hybrid approach, but it has no natural answer for the exception minority. VAT edge cases and policy ambiguities require genuine human interpretation, and a fully automated system would either post those cases incorrectly or reject them without a structured escalation path. The client has also been clear that they want people focused on the cases that need them, not removed from the process entirely, and a pure AI Workflow without human gates does not match that intent.

Traditional Code scores five and deserves a mention because the ERP API is available and the policy rules are documented well enough for rule-based logic to work on the structured portion of the process. The problem is receipt data. Receipts arrive as images and PDFs, and rule-based code has no practical way to extract amounts, categories, dates, and VAT figures from unstructured documents without an AI layer sitting in front of it. Adding that layer to a Traditional Code solution essentially recreates the Hybrid architecture at greater development cost and with more fragility. RPA scores four and is not recommended: since the ERP already has an API, RPA's primary use case does not apply, and the maintenance burden on a small team without in-house developers would be disproportionate to any benefit it offers.

The build starts with a policy codification sprint of one to two weeks, running workshops with the finance team and the external accountant to capture every travel policy rule in a structured decision table. This is not a technical task but it is the most important one: any ambiguity left undocumented at this stage will show up as a false exception flag or, worse, an incorrect auto-approval after go-live. The output is a written rulebook covering claim categories, per-diem limits, receipt requirements, and VAT treatment, alongside a confirmed map of the ERP API endpoints needed to read claim submissions and write approved postings.

With the rulebook confirmed, the external developer builds the AI document-processing layer. A service such as Google Document AI, AWS Textract, or a combination of an OCR tool with a GPT-4o extraction prompt handles the conversion of receipt images and PDFs into structured JSON fields: amount, date, merchant, category, and VAT amount. Those extracted fields are then run through the policy rulebook programmatically, and each claim receives a pass, fail, or exception flag with the specific rule cited. The workflow platform connecting these components can be Make or n8n if a low-code approach suits the developer, or a lightweight Python service deployed on something like Railway or Render if a more custom build is preferred. The choice should be driven by the external developer's recommendation given the ERP's specific API characteristics, not by platform brand preference.

The ERP integration stage, taking two to three weeks, connects the policy check layer to the ERP API so that claims flagged as pass are posted automatically. Critically, every posting writes a machine-readable audit log entry alongside it, recording the rule version applied, the extracted data values, the confidence scores from the document AI, and the timestamp. This audit log must live in durable storage from day one, whether that is a database attached to the workflow platform or a structured export to the ERP's own document store, and it must satisfy the seven-year retention requirement without manual intervention. The external accountant should review sample postings against VAT rules before anything goes live.

The exception routing interface is deliberately kept simple at this scale. A lightweight web form built in something like Tally or a low-code tool, or even a structured email routed to a shared inbox, surfaces each flagged claim to the finance team with the extracted data and the escalation reason pre-populated. The reviewer approves, corrects, or rejects, and that human decision is written back to the audit log alongside the original automated assessment. After a two-to-four week parallel run where automated outputs are compared against the existing manual process on a representative sample of real claims, the external accountant and the finance team sign off on the VAT accuracy and audit trail quality before the team cuts over fully. Exception routing thresholds are tuned during this period based on observed false-positive rates.