Building Dashboards that Spotlight Fraud and Friction

## The $50K Hallucination Imagine this: your AI model just hallucinated in production, leading to a $50K hit in customer refunds due to false positives in your hiring pipeline. Such scenarios are becoming all too common as organizations rush to automate their hiring processes without proper guardrails. Engineering and,

data teams must be proactive in deploying robust dashboards that not only spotlight leakage—where candidates slip through the cracks—but also friction, where legitimate candidates are wrongly rejected. The stakes are high; a single misstep can cost your company not just financially, but reputationally as well. ## Why

This Matters For engineering leaders, the importance of addressing these issues cannot be overstated. Every false accept and reject can lead to wasted resources, lost productivity, and compromised security. Moreover, as the hiring landscape becomes increasingly competitive, the ability to streamline processes while uph

olding integrity is essential. By focusing on leading indicators that predict drop-off and fraud attempts, you can create a hiring environment that minimizes risks and optimizes outcomes. ## How to Implement It 1. **Identify Key Metrics:** Start by determining which metrics will be most indicative of leakage and fraud

attempts. Key performance indicators (KPIs) might include false accept rates (FAR), false reject rates (FRR), and completion rates of application processes. 2. **Set Up Real-Time Dashboards:** Utilize tools like Tableau or Power BI to create interactive dashboards that visualize these metrics. Ensure they are updated

in real-time to provide immediate insights into hiring performance. 3. **Incorporate Telemetry Data:** Integrate telemetry data from your hiring systems to track candidate behaviors. This can help you identify patterns that lead to drop-off or fraud attempts, allowing you to adjust your strategies proactively. 4. **Rev

iew and Adjust Staffing:** Use insights gained from your dashboards to inform reviewer staffing decisions. For example, if data shows that certain times of the day have higher false rejects, consider reallocating resources to those peak times. ## Key Takeaways - Always validate AI outputs against real-world outcomes.