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Risk profiling using predictive analytics for a leading airport solution provider

Problem Statement

Client wanted to test and build an innovation proof of concept to predicting the valid hit (Rule-in hit), using the historical patterns of the travellers and profile available in the system (idea was to improve accuracy of predictions in comparison to rule based approach that is deployed currently).

Solution Overview

A machine leaning based approach was adopted to reduce the False Positive rate for the hit generation. The objective was to increase potential rule-in hit cases of new travellers crossing the border.

  • Leveraged open source R, Python for modelling
  • Clustering, Time Series & Regression algorithms used


  • Increased probability of identifying a risky passenger
  • False Positive Rate is improved by 77%
  • Overall hit accuracy is also improved by 2 bps

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