Duncan Anderson
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Production enterprise systemData scientist and system builder while employed

Travel operations

Dispute Defender

Turn a high-volume dispute queue into evidence-backed responses.

Repetitive investigation became a repeatable operating system, allowing more disputes to receive specific evidence instead of a generic response.

Operational proofEvidence view

Case 02491

Service provided

Review
Booking record2 records
Payment history3 records
Customer communication4 records

Evidence package

Ready for operator review

Sources linked

8 / 8

Missing evidence

None detected

A person approves the final response. Source records remain attached to the case.

Representative workflow · identifying data removed

What changed

Instead of asking an operator to reconstruct every case from several systems, the pipeline prepared a traceable evidence package and left the reviewer with a focused decision.

Before

  1. 01Operators searched booking and payment records case by case.
  2. 02Evidence quality depended on available time and individual judgment.
  3. 03High queue volume encouraged generic or incomplete responses.

After

  1. 01Relevant records are gathered into one case context.
  2. 02The dispute reason drives the evidence and response structure.
  3. 03The final package remains connected to its underlying sources.

What I owned

A production pipeline gathered booking and transaction evidence, classified dispute context, and assembled tailored response packages for an online travel business.

  • 01Evidence retrieval and data normalization
  • 02Case classification and response assembly
  • 03Operational pipeline design
  • 04Production reliability and exception handling

Where trust was designed in

The system organized evidence and prepared a defensible response; it did not manufacture facts. The public visual is representative because the original company and customer data are confidential.

Evidence

  • Built for high-volume operational use
  • Evidence remained traceable to source records
  • Production enterprise deployment

Technical detail

PythonMachine learningREST APIsData pipelines

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