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Public Sector and Government Data Analytics: Border Security

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Use Case

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Border security agencies use targeting systems to analyze movement and identify potential threats. Legacy targeting systems have high levels of false positives—up to 90%—as they depend heavily on rules-based pattern matching to identify passenger and cargo risk. This approach leads to lower productivity, risks to travelers, endangers the security of citizens, and impacts the collection of tariffs.

Industry Challenge

Protecting Borders and Safeguarding Travel

The rapid increase of cross-border travel and trade is increasingly difficult to track and manage in globalized economies. To provide secure transport of people and goods across countries, governments are strengthening external borders and investing in technology to support safe and efficient screening.

Border security agencies use targeting systems to analyze movement and identify potential threats. Legacy targeting systems have high levels of false positives—up to 90%—as they depend heavily on rules-based pattern matching to identify passenger and cargo risk. This approach leads to lower productivity, risks to travelers, endangers the security of citizens, and impacts the collection of tariffs.

Data Analytics Application

Making Faster and More Accurate Decisions in the Border Crossing Process

Unisys advanced analytics solutions combine rules-based and predictive algorithm models to effectively focus on cross-border movement that poses a potential security risk, while allowing low-risk passenger and cargo clearances with minimal interference.

There are two categories of rules-based models: user-defined by border security agency experiences or temporary situational needs, and engineered rules, which are built into the system based on Unisys’s years of experience and industry best practices working with various national and regional border protection agencies. These rules are enhanced by predictive algorithms that search and learn patterns in existing data for application in future scenarios. Predictive models do not generate manual rules, but identify potentially risky behaviors from past observations to establish a baseline of normal activity. With this baseline, anomalies are easier to detect and flag for manual intervention and closer supervision to ascertain any real threat.

The data analytics solution uses a continuous feedback loop to learn about high-risk behaviors and improve the accuracy of targeting. It also analyzes traveler and cargo data prior to arrival to determine if the subject represents a possible security threat.

Business Value

Advancing Border Security Technology via Data-Driven Analytics

Facilitating global travel and trade, while mitigating threats such as drug and human trafficking, smuggling and terrorism, requires the latest technology powered by advanced data analytics and machine learning. This data-driven approach helps governments stay ahead of adversaries by predicting threats in near real-time before they occur.