The Swan Effect: What Banks Really Look Like Beneath the Surface
Swans: they’re known for their grace and calm movements. They’re composed, elegant, and oftentimes described as being majestic for their ability to glide silently across a lake.
But below the surface of the water, swans are paddling hard and heavy. Their rhythmic movements repeat circular motions that propel the swan forward. While the swan looks effortless to people on land, underneath the water looks entirely different.
Similarly to the outside world, banks and financial institutions often appear calm, stable, and highly controlled. Like a swan gliding across a lake, everything above the surface looks effortless. But like the swan, beneath the waterline is again, something very different.
Customers experience mobile apps, payment confirmations, faultless trading, and account balances instantly. They aren’t privy to the thousands of staff, manual spreadsheets, fragmented and legacy systems that are working to emulate a stable and seamless customer experience.
While the elegance of a well-oiled machine is what we see on the outside, the opportunity for true AI scale is beneath the surface. Submit the below to learn how you can use AI safely.
Let’s look at Capital Markets. While a trade may appear complete the moment a trader clicks “execute”, the reality is that there are teams spending hours managing trade breaks, resolving settlement exceptions, reconciling positions across multiple systems, and handling collateral disputes between counterparties. One small discrepancy in trade details is enough to inspire a full on investigation where approvals and manual interventions across operations, risk, compliance and tech teams are pulled in.
The same applies to health insurance companies. While a claim might appear straightforward to a customer, behind the scenes, providers are busy adjudicating claims, validating payments, investigating exceptions, and processing documentation.
Similarly, while a customer might find they were able to open a new account or file a dispute, teams are managing fraud investigations, processing KYC (Know your Customer) remediation, and are handling AML (anti-money laundering) reviews.
These tedious workflows that happen in the background is the core of what keeps banks and these types of highly regulated organizations afloat. While most of this work is hidden from customers, it's essential for maintaining the stability, trust, and reliability that banks are known for.
Like the paddling beneath a swan, these activities rarely attract attention when they work well but they are constantly in motion. On the contrary, when these activities break, customers are usually the first to notice.
While things look good from the outside, banks and other regulated organizations struggle with traditional automation. For something to work well, workflows need to be linear, inputs need to be structured, and rules need to be deterministic. But this rarely measures up to the reality of these institutions. Bank operations, specifically, are known for being non linear, exception heavy, context dependent, and constantly evolving.
Because of these factors, traditional automation can create a larger headache than the manual workflows already in place. Traditional automation implies cutting the human out completely, but in these industries, agentic AI and humans need to work together to connect themselves between different systems and departments.
Artian sits at the intersection of banks, financial institutions, and other highly regulated industries who desperately need automation but also rely heavily on domain expertise and governance.
When we built Artian we kept context at the forefront. Our agents coordinate workflows with exception context built-in, and escalate to humans when judgment is needed. Every step is explainable so you know exactly how your agents got to the answers they did, all while maintaining governance and controls that stand up to internal audits and regulators.
There's a constant whirl of AI conversations, but leaders in the space know that the futures of banks are not fully autonomous and human-free. Instead, they are AI orchestrated, governed by humans, and they are operationally and domain-intelligent. The banks that get it right will use AI in a safe and scalable way, and implement AI assistants that are embedded inside their own operational processes.
Artian helps banks who face margin pressure, aging infrastructure, and increasing customer expectations. Having been on the other side, we know operations teams are overloaded and that institutional knowledge isn’t equal and is often fragmented.
While the elegance of a well-oiled machine is what we see on the outside, the opportunity for true AI scale is beneath the surface.
The institutions that succeed in the next decade will not be the ones that merely appear calm above water, but they will be the ones that orchestrate the hidden layer and paddling underneath.
