🧠 Detecting Tax Fraud Before It Happens: How Predictive Analytics Enhances Government Cybersecurity

By First To Invest
June 2025

As government services become increasingly digital, fraud evolves with them. Today’s attackers don’t rely on brute force—they exploit verified identities, slip past traditional defenses, and operate in ways that look almost legitimate.

At First To Invest, we specialize in transforming large-scale open-source data into actionable intelligence. Our work in fraud analytics and behavior modeling equips federal agencies with the tools to stay ahead of these evolving threats. Here’s a glimpse into how we’re applying predictive analytics to protect digital identity systems—before fraud occurs.

🎯 The Challenge: Fraud Hidden in Plain Sight

Modern identity fraud often originates from compromised data outside government systems—yet it directly targets critical applications like the IRS’s “Get Transcript” and IP PIN portals. These services are protected by the Secure Access Digital Identity (SADI) framework, designed for high-assurance authentication.

But even a verified login can be fraudulent if the identity was stolen.

These “low-and-slow” threats mimic normal users, making them invisible to legacy security tools.

🧪 Our Approach: Modeling the Attack Before It Happens

To simulate real-world attack scenarios, we built a synthetic dataset of 500 login sessions, each containing:

• IP address and geo-location (e.g., U.S., China, Russia, Iran)

• Input velocity (keystrokes/second)

• Session duration

• Device ID, risk score, and behavior metadata

• Fraud label for model training and evaluation

This emulated a typical government fraud-monitoring environment, ready for analysis.

⚙️ The Model: Predicting Anomalies Using Machine Learning

Using a Random Forest Classifier, our fraud detection pipeline identified patterns of anomalous behavior based on three key indicators:

• Input velocity (how fast users typed)

• Session duration (how long they stayed active)

• Risk score (based on IP and behavioral intelligence)

The model achieved:

• 89.3% accuracy

• 85.7% precision for fraud detection

• Sub-2-hour alert time for fraud cases

These results demonstrate a system capable of detecting behavioral outliers well before malicious actors succeed in their objective.

📈 Real-World Impact: From Detection to Prevention

In practice, this system could:

• Monitor high-volume user sessions in real-time

• Send alerts to IRS cybersecurity teams

• Support forensic analysis and policy updates

• Feed new indicators back into the model for future resilience

Fraud events are no longer just reactive—this pipeline shows how analytics can make them predictable and preventable.

💡 Why It Matters

Government systems face billions of dollars in fraud-related losses every year. At First To Invest, our hybrid model of AI-driven detection and expert OSINT analysis allows agencies to shift from defense to anticipation.

We don’t just surface anomalies—we provide mission-aligned insight with the speed and scale required for federal cybersecurity operations.

Whether you’re protecting financial systems, national data, or digital citizen services, proactive intelligence is the frontline—and we’re here to deliver it.