AI in Wealth Management: How ChatGPT and Anthropic are shaking up the traditional banking relationship
Friedhelm Schmitt
In the debate around Artificial Intelligence in Wealth Management, the focus is almost always on tools, assistant systems, automation and efficiency. Friedhelm Schmitt, co-founder and CEO of fincite, believes this is the wrong perspective. The real question is not which tools banks use, but what their business is actually based on, and this advantage has already shifted. With fincite • cios, fincite develops a modular wealth management software that is used by over 9,000 wealth managers in Europe, and is part of the Harvest Group.
Banks' information advantage is no longer exclusive
For decades, a bank's business model was based on an information advantage. The bank understood markets, products and financial contexts better than its clients, and the value of advice arose from this asymmetry. Today, when providers like OpenAI integrate financial functions directly into ChatGPT or Anthropic brings its model to corporate finance departments, the logical conclusion is that the traditional banking relationship is starting to waver.
However, a software positioning itself between the provider and the customer is not a new phenomenon. We experienced it in the travel business with Booking, in retail with Amazon, and in banking itself with comparison portals like C24.
What is remarkable is something else: for the first time, a system is ready that can capture a person's financial situation more comprehensively than a bank currently does. Disintermediation by an intermediary is therefore not the real threat. The decisive factor is that understanding the customer is no longer exclusive to the bank.
Why categories like risk classes no longer represent clients holistically
The way banks capture their clients today remains remarkably coarse-grained. A client is classified by age, risk class and investment horizon, and their advice is derived from this classification. They are then deemed risk-tolerant or risk-averse, as if this were a permanent characteristic rather than a snapshot in time. This has little to do with a person's reality, and even less with how modern systems function.
An Artificial Intelligence can holistically grasp what stage of life a person is in, how their situation is changing, and when they are about to make a decision against their own interests. In doing so, it already provides a more nuanced assessment than the traditional advisory process in most banks.
Therefore, those wealth managers who understand their role as a platform will prevail. They accompany the client across their entire wealth, intervene situationally, and build trust over long periods of time, instead of pigeonholing the client once a year into an existing template.
The three layers of the banking stack in the AI era
Anyone who thinks through the technological structure of the AI era recognizes three sequential layers. For banks, the decisive factor is not whether these layers exist, but which of them they control themselves.
The data aggregation layer forms the foundation because language models and business applications only unleash their impact once they can access reliable customer data.
The business applications and AI layer generates the actual utility, either through the bank itself or via partnerships, and is complemented by the models above it.
The regulatory and compliance layer must be controlled by banks themselves, as it is the only one that ensures everything above it remains trustworthy, auditable, traceable and regulatorily sound.
This last point in particular is underestimated in the current discussion, and only a few banks have it strategically on their radar. So far, the industry has mainly been busy preparing and displaying information. However, the next development step of AI lies in action: in advisory systems that execute recommendations autonomously, and subsequently in the active management of portfolios. Very few institutions are prepared for this development so far.
Wealth aggregation as a strategic factor
Europe has missed a strategic opportunity with Open Banking. In terms of both regulatory implementation and political lobbying, the opening up of financial data was slowed down rather than accelerated. However, developments in the United States clearly show where the journey is heading: the aggregation of financial data will happen, and the only open question is which player it will run through.
As long as banks or regulated aggregators control this layer, they retain sovereignty over access, authorization, governance and customer consent. This allows them to retain actual control over who can use which data and for what purpose.
For this reason, bank-operated wealth aggregation is the first concrete step. It is the gateway to the AI layer and belongs under the responsibility of the bank. Those who give it up risk becoming pure infrastructure in the long term, while others shape the customer relationship. fincite • cios is built precisely around this layer – a modular wealth management software that is API-first and can be integrated into existing core banking systems.
Memory as the next developmental stage of advice
The most strategically far-reaching aspect concerns the ability to store context over time. I consider this form of memory to be the next big developmental stage of Artificial Intelligence. Human identity is essentially created by the fact that experiences shape us, that we connect them over the years and derive patterns from them. Today's systems do not yet fully possess this capability, but development is clearly moving in this direction.
It is precisely at this point that the vulnerability of banks becomes particularly clear. The image that a bank has of its customer is usually limited to income, risk class, marital status and a few meeting notes. It remains a static snapshot that neither corresponds to a person's reality nor to the logic by which future AI systems will operate.
What banks therefore need to build is a layer that captures a customer's context across their entire life cycle, from major life events to shifting market phases and changes in behaviour. Only on this basis does individual advisory become possible.
Conclusion: What Artificial Intelligence in Wealth Management means for European banks
Artificial Intelligence in Wealth Management has reversed the information asymmetry that banks have lived on for decades. The advantage is no longer naturally with the bank, but with the system that understands the customer best. Anyone who wants to regain this advantage must keep data aggregation, the compliance layer and client memory within their own responsibility. Wealth aggregation is the first concrete step towards this, and the technology required is ready. The decision is in the hands of the banks themselves.
