The Untapped Edge: Client Exhaust and Passive Data in Institutional Finance

For global financial institutions, success increasingly depends not just on managing capital, but also on turning vast amounts of anonymized client data into actionable intelligence – to help our clients by enhancing execution, deepening market understanding, and improving client engagement. 

As technology advances, so too has our definition of what data is worth capturing. Leading institutions now extract value from the full spectrum of client behavior – logins, trade inquiries, market data requests, execution patterns, and other digital signals that surround a financial decision. 

This passive data, once dismissed as background noise, or “exhaust” in industry terms, is proving to be a missing link in the modern data stack. It reveals the thought process behind a transaction, offering insights that move beyond backward-looking analysis. When harnessed correctly and in a responsible and ethical manner, client exhaust brings financial analytics closer to the holy grail: models that not only explain what happened, but also predict what’s coming next. 

In many ways, "client exhaust" is not a new concept. It extends a familiar idea from consumer sectors such as hospitality and streaming entertainment. For a while now, bank IT teams have aspired to replicate this kind of intuitive, anticipatory service – using passive data to better understand client needs. 

The challenge in financial services has always been the variety, complexity, and sensitivity of the data. Historically, building models meant starting from scratch: coding the logic, securing compliance approval, layering in machine learning so the model could continuously educate itself on real-world behaviors, and testing in a controlled environment. Projects could take years to launch, sometimes missing the moment they were designed for. 

Today, that timeline is shrinking. With access to vetted generative AI libraries, teams can identify existing agents capable of handling self-directed tasks and move more quickly to implementation. This acceleration is opening the door to more dynamic and responsive uses of client exhaust across the bank. 

A good example comes from our Global Markets division. Institutional investors interacting with broker-dealer platforms generate great clouds of client exhaust: what time they trade, whether they break orders into smaller pieces, which venues (dark pools, exchanges, internal liquidity) they prefer, their price sensitivity, and their tolerance for cost versus speed. On their own, these data points don’t say much. But combined and modeled with AI, they enable us to construct detailed execution personas and simulate thousands of hypothetical clients. 

With these personas, we can run simulations across varied market conditions to predict how different client types might respond. That, in turn, informs how we route trades in real time. A smart order-routing system like the one that BMO has been using for years suddenly becomes smarter and more perceptive.  

With greater power comes a responsibility to use it wisely. Client exhaust must never be linked to confidential account data or position data protected by a Privacy Impact Assessment (PIA). That’s where our internal data governance protocols come into play. These boundaries must remain strong and visible to ensure that client exhaust builds trust rather than erodes it. 

Used responsibly, client exhaust can help us fine-tune pricing models, identify when tighter spreads or more competitive rates are appropriate, and share insights that would otherwise remain buried. The concept is not so different from the handwritten notes traders once used to track client preferences – only now, it scales. 

Looking back, I realize I’ve been thinking about client exhaust since my early career, when I was the third employee at a startup that was building automated order generators in 1999. We had ambition but lacked the data infrastructure, analytics capability and AI toolkits that exist today. 

Now, we have all of that. What we need next is a cultural shift. Our data teams have made great strides in building trust with internal partners and shortening the runway from insight to implementation. But the real opportunity lies in getting client-facing teams – traders, relationship managers, wealth advisors, investment bankers – to think this way, too. 

Client exhaust isn’t a niche data science project anymore. It’s becoming a foundational element of how we operate. The firms that embrace this mindset today will define the client experience of tomorrow.