Background: A UK Fintech’s Back-Office Bottleneck
This case study is a composite based on patterns observed across multiple engagements. We do not name real clients. The company described here is a mid-size UK fintech operating a payments platform for B2B clients, with 1,200 employees across London and Manchester. The back-office operations team handled supplier invoices, payment reconciliation, and vendor onboarding. The stack included a UK-hosted ERP, a Zendesk helpdesk, a custom payments gateway, and a mix of spreadsheets and manual data entry for invoice processing. The company had already deployed a basic RAG assistant over its internal documentation but had not touched invoice processing. The operations director flagged that the back-office error rate had crept to 3.8% over the prior two quarters, driven by data-entry mistakes in vendor codes, tax fields, and payment terms. Each error triggered a reconciliation cycle that averaged 6.5 business days. The board had set a target: reduce the cost per support ticket and the back-office error rate within one fiscal quarter, without adding headcount. The compliance team confirmed that any solution touching invoice data had to satisfy PCI DSS Requirement 3.5.1 (no full PAN storage) and the client’s internal data-residency policy, which prohibited sending invoice data to any third-party API outside the UK.
Challenge: PCI DSS, Data Residency, and an 8-Week Deadline
The operations director’s brief was specific: cut the back-office error rate from 3.8% to under 1% within 8 weeks, without adding headcount, and without sending invoice data to any third-party API. The compliance team added a hard constraint: PCI DSS Requirement 3.5.1 prohibited storing the full Primary Account Number on any system, and the client’s internal data-residency policy meant no invoice data could leave the building. The timeline was fixed by the board’s fiscal-quarter deadline. The team had 12 back-office staff processing roughly 4,200 supplier invoices per month across three departments. The manual process involved scanning PDFs, keying data into the ERP, and flagging discrepancies for review. The error rate was not uniform: vendor-code mismatches accounted for 40% of errors, tax-field mistakes for 30%, and payment-term misclassification for the remaining 30%. The operations director also wanted a measured before/after baseline on cycle time and error rate, not just a qualitative improvement. The challenge was not whether an LLM could read an invoice; it was whether the system could do so inside a PCI DSS boundary, on the client’s own hardware, with a human approval step for anything touching a payment amount.
Approach: n8n Orchestration with a Local LLM and Human-in-the-Loop Approval
The engagement started with a two-week process audit. We mapped the invoice lifecycle from receipt to payment, identified the three error-prone steps (data entry, classification, and discrepancy flagging), and measured the baseline: median cycle time of 4.2 days, error rate of 3.8%, and an average of 11 minutes of manual work per invoice. The architecture was model-agnostic by design. The n8n workflow ran on the client’s own VPS in a UK region, orchestrating the pipeline: pull invoice from the ERP via a custom REST endpoint, strip any PAN fields before the document reached the model, call a local Llama 3 70B on the client’s A100 GPU, validate the output against a JSON schema, and push the structured data back to the ERP via webhook. The helpdesk integration used Zendesk’s REST API to create a ticket when a human approval was needed. The human-in-the-loop step was non-negotiable: any field touching a payment amount above GBP 5,000 or a contract clause required a reviewer’s sign-off. The n8n workflow logged every approval action with a timestamp, so the team could measure reviewer latency and field-level changes. The pilot covered one invoice category (supplier invoices in GBP, under GBP 25,000) and one department (AP).
Outcome: 0.9% Error Rate, 1.1-Day Cycle Time, PCI DSS Sign-Off
The pilot ran in shadow mode for six weeks: the model processed every invoice in parallel with the manual process, and the team compared outputs. After shadow mode, the system went live with human-in-the-loop approval for the first two weeks, then gradual autonomy. The measured outcomes: median cycle time dropped from 4.2 days to 1.1 days; the error rate fell from 3.8% to 0.9%; and the approval queue shrank to 12% of volume after six weeks. The cost per support ticket in the back-office context (reconciliation time plus late-payment penalties) dropped from an estimated GBP 180-240 per error to under GBP 40. The 12 back-office staff were not laid off; they were redeployed to handle the 12% of invoices that still required human review, plus new vendor onboarding tasks that had been backlogged. The n8n workflow handled 88% of invoices end-to-end without human intervention. The model never saw a full PAN; the n8n workflow stripped PAN fields before the document reached the model, and the output schema rejected any field containing a 13- to 19-digit numeric string. The client’s PCI DSS assessor signed off on the architecture in the final week of the pilot.
Lessons for Teams Scaling AI Across Departments
Five lessons from this engagement generalize to similar teams scaling AI across departments in regulated environments. First, the process audit is not optional. The two-week audit identified that 40% of errors came from vendor-code mismatches, which a generic OCR solution would have missed. The n8n workflow included a vendor-code validation step that cross-referenced the ERP’s vendor master before the model even ran. Second, model-agnosticism is a risk hedge, not a buzzword. The team swapped from Llama 3 70B to a smaller 8B model for a specific document type (credit notes) where the 70B was overkill and the 8B was 3x faster on the client’s hardware. The n8n workflow logic did not change. Third, the human-in-the-loop step must be measurable. Logging every approval action with a timestamp let the team prove that reviewer latency dropped from 11 minutes to 2.3 minutes per invoice as the model’s accuracy improved. Fourth, the 8-week timeline was only achievable because the pilot scope was fixed to one invoice category and one department. Trying to cover all three departments in 8 weeks would have pushed the timeline to 14 weeks. Fifth, the managed operations contract was not an afterthought. The 12-month post-rollout contract covered model monitoring, prompt tuning, and n8n workflow maintenance, which kept the error rate at 0.9% rather than drifting back to 2% as invoice formats changed.
Leave a Reply