Enhancing Risk Profiling with Predictive Analytics for a Leading Airport Solution Provider

Challenges.

The client faced significant challenges in their risk profiling process:

• Testing and Building a POC: Needed to test and build an innovative proof of concept for predicting valid hits.
• Improving Accuracy: Aimed to improve the accuracy of predictions compared to the current rule-based approach.
• Reducing False Positives: Required a solution to reduce the false positive rate for hit generation.

Our Solution.

Coforge implemented a comprehensive predictive analytics solution:

• Machine Learning Approach: Adopted a machine learning-based approach to reduce the false positive rate for hit generation.
• Modeling Tools: Leveraged open source R and Python for building predictive models.
• Advanced Algorithms: Utilized clustering, time series, and regression algorithms to improve prediction accuracy.

The Impact.

• False Positive Rate Reduction: 77% improvement.
• Hit Accuracy: Improved by 2 bps.
• Risk Passenger Identification: Increased probability.