Artificial intelligence has moved from hype to substance in finance. For quantitative analysts, it has become a tool that not only improves efficiency but expands the very boundaries of what is possible in markets.
“AI doesn’t just make existing processes faster,” explains Axel Goetz, a quant with extensive experience in strategy design. “It changes the kind of questions we can ask, and the signals we can find.”
Mining Signals in the Noise
Quants have always searched for predictive signals — patterns that indicate how markets might move. The difference today is the data itself.
“Ten years ago, most models were built on clean, structured financial data,” Goetz says. “Now, we can look at unstructured sources and actually extract meaning. That might be news sentiment, shipping records, satellite imagery, …. These are messy inputs, but with the right methods, they become powerful predictors.”
This ability to transform noise into signal is perhaps the most important shift AI has introduced. It expands the universe of data that quants can explore, creating strategies that would have been unthinkable in the past.
Correlations Hidden in Plain Sight
Beyond signals, AI has enabled quants to uncover relationships between markets that were previously invisible.
Goetz explains: “Traditional correlation analysis often misses non-linear dynamics. AI models can capture the subtle dependencies that only show up during stress events or in specific environments. That kind of insight can fundamentally change how a portfolio is built.”
These hidden links matter for both return generation and risk management. By revealing connections between asset classes, AI helps funds anticipate vulnerabilities before they appear in standard models.
The Challenge of the Black Box
Of course, the power of AI also introduces complexity. Many of the LLM models are “black boxes,” offering predictions without transparency.
Goetz sees this as one of the field’s defining challenges: “If you have a model telling you to place a large trade, you need to know why. Otherwise, you’re just gambling with very sophisticated tools. Interpretability is not optional — it’s essential.”
Explainable AI is now a frontier within quantitative finance. Goetz believes the firms that can balance accuracy with clarity will have the most durable edge.
More Than Just Trading
AI’s impact in quant finance goes far beyond execution. Risk monitoring, compliance, and even operational resilience are being reshaped.
“Markets aren’t just about making money — they’re about preserving it,” Goetz notes. “AI can flag anomalies before they escalate, giving risk teams a chance to act early. That’s as valuable as any alpha signal.”
For him, the story of AI is not only about trading strategies, but about strengthening the entire financial ecosystem.
Looking Ahead
The integration of AI into quantitative finance is still evolving. For Goetz, the future lies in a balance of bold experimentation and disciplined oversight.
“We are only at the beginning of this transformation,” he concludes. “The quants who will define the next decade are the ones who don’t just use AI, but truly understand its limits and build responsibly around them.”