# AI-Driven Revenue Recovery for Global Airport Services Operator

> Global Airport Services Operator · Industrial & Aviation · QuantSpark Labs

QuantSpark helped a global airport services operator identify £1.5M in potential revenue leakage within four weeks using a Generative AI-powered solution for invoice reconciliation.

## At a glance

- **£1.5M** Potential Revenue Leakage Identified
- Engagement: 4 weeks

## What was the problem?

A global airport services operator was facing significant financial losses due to **revenue leakage**, estimated to be millions of pounds annually. This leakage stemmed from **underbilling customers** for services that had been delivered. The core issues driving this problem were:

*   **Inaccurate service capture information**: Gaps and errors in recording the actual services provided.
*   **Flawed implementation of contractual billing logics**: Discrepancies between agreed contract terms and actual billing practices.

Historically, the process of recovering this lost revenue was **manual, non-exhaustive, and inconsistent**, limiting its scale and overall effectiveness.

## What did QuantSpark do?

QuantSpark was engaged to conduct a **4-week feasibility study** to explore whether a Generative AI-powered solution could effectively support the reconciliation of invoiced revenue to expected revenue. Our innovative approach involved several key stages:

*   **Impact Document Extraction**: We used **multi-modal Large Language Model (LLM) techniques** to accurately extract critical information such as prices, clauses, and conditions directly from contractual documents. This included processing challenging formats like manually scanned PDF files.
*   **Service Capture Matching**: Utilising advanced **natural language processing (NLP) techniques**, we dynamically mapped the service capture information (what was delivered) against the extracted contractual information (what should have been billed).
*   **Discrepancy Identification**: Our system then compared the invoices generated based on our analysis with the historic issued invoices. Any identified discrepancies were meticulously categorised by their underlying driver – for example, an agreed price change or a billing logic inaccuracy. This categorisation was crucial for triggering and supporting subsequent financial and commercial auditing investigations.

This comprehensive, AI-driven algorithm was designed to bring transparency and accuracy to a previously opaque and error-prone process.

## What changed?

The 4-week feasibility study yielded highly impressive results, demonstrating the significant potential of our AI-driven solution:

*   **£1.5M of potential revenue leakage identified**: The algorithm successfully pinpointed substantial underbilling.
*   **42,000 invoiced lines processed**: Discrepancies were found within this large dataset.
*   **16% discrepancy rate**: A significant proportion of the processed lines showed issues.
*   **Coverage**: The solution was applied across **3 airlines** and **2 international airports**, showcasing its applicability across different operational contexts.

This engagement proved that a Generative AI solution can effectively and efficiently identify and categorise revenue leakage, providing the client with actionable insights to recover lost revenue and improve operational excellence.

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