SHIPMENT RISK MONITORING

An anomaly-detection system built to watch every consignment in motion, surface exceptions early and keep freight operators ahead of loss.

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Case detail
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Project challenges

Spotting trouble across thousands of moving consignments calls for precise anomaly detection and firm regulatory control. Messy operational data made that harder.

  • Separating real risk from daily noise.
  • Handling continuous shipment feeds at volume.
  • Meeting customs rules across every jurisdiction.
  • Holding response times inside secured networks.
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The solution

We built one system that sits on top of the operator's existing freight platform. It learns what a normal shipment looks like, picks out the ones that do not fit, and says what to do about them.

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The result

The operator catches problems earlier, argues fewer disputes and loses far less to exceptions.

60

%

Lift in anomaly detection accuracy

25

X

Live consignment screening

99

%

Regulatory checks passed cleanly

Project information

Date:
Jul 2023
Client:
Freight & Logistics Global Middle East
Industry:
Logistics
Services:
Machine Learning, Dashboard Engineering, Security Audits
Technology stack:
Python, Scikit-learn, AWS Lambda, MySQL

“It earned its place the first month. Two consignments we would otherwise have written off.”

Mohammed Ramiz
Vice President
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