Recent posts by Mona

Artificial intelligence (AI) and machine learning (ML) models drive mission-critical decisions in industries ranging from finance to healthcare. Without robust monitoring, these models can degrade over time, leading to poor performance, biased predictions, and even compliance risks. That’s why model performance monitoring is a critical component of AI infrastructure, ensuring both AI reliability and business impact.

But how do organizations evolve from basic monitoring to fully integrated, proactive AI observability? One way to think about it is through a four-stage maturity curve, which starts with simply collecting relevant data and ends with full monitoring operations. Understanding these stages can help organizations assess where they stand and what steps to take next.

Backtesting is the backbone of quantitative finance, enabling quants to simulate strategies and assess performance in a controlled environment. But as any seasoned quant will tell you, much like a model is only as good as its representation of the real world, a backtest is only as good as its alignment with real-time trading. The leap from simulated strategies to live markets often reveals discrepancies that can erode profits, undermine confidence, and even jeopardize entire strategies.

Why do these gaps between backtesting and real-time trading occur? And more importantly, how can they be addressed? In this blog, we explore the common pitfalls that create these discrepancies, the role of intelligent monitoring in closing the gap, and how Mona helps quants detect and address issues before they impact the bottom line.

If you’ve ever been responsible for monitoring models at a quant hedge fund, you know how tricky it can be. One minute everything looks fine; the next, you’re in a full-blown fire drill because of a performance issue you didn’t catch in time. In other cases, you learn way too late that there was something simple you could have changed that would have improved your results significantly, or some specific area that was completely overlooked. But why does this happen? The problem often comes down to granularity—or rather, the lack of it.

In the world of automated trading, quantitative (quant) models are at the core of decision-making. These models analyze vast datasets to execute trades at speeds and volumes that far exceed human capability. However, ensuring that these models consistently perform well requires effective monitoring. Traditional approaches to performance management, which focus on broad financial metrics, IT infrastructure, and standard machine learning (ML) monitoring, are often insufficient. Specialized, deep monitoring is necessary to truly understand how these models behave and to maintain their effectiveness over time.