AI systems can analyze portfolios, identify advisory triggers, and prepare recommendations. Whether these recommendations actually fit a client’s situation depends on the underlying data. André Rabenstein, Co-founder and CEO of wealthAPI, discussed this connection in episode 29 of the “Schnittstelle” podcast by Payment & Banking. Moderator Sarah Schütz recorded the conversation live at Banking Exchange 2026 in Frankfurt.

The Starting Point: Fragmented Wealth Data

A checking account at a primary bank, an ETF savings plan with a broker, a private pension contract with an insurer: many people’s finances are spread across multiple institutions. Advisors usually only see the portion managed within their own firm. Any analysis based on this remains incomplete, regardless of whether a human or an AI system performs it.

As a BaFin-licensed account information service provider, wealthAPI aggregates account and custody data from various sources, including portfolio positions, securities transactions, and tax-relevant information. wealthAPI clients—such as banks, fintechs, and asset managers—process this data within their own applications.

From Transaction Data to Advisory Triggers

The podcast focuses primarily on how aggregated data creates concrete advisory triggers. Changes in account movements allow for conclusions about life events, such as a move, a large incoming payment, or the birth of a child. This determines which topics are relevant for a discussion and which are not. An investment proposal makes little sense as long as the account is in overdraft. Conversely, a high cash inflow can be a reason to discuss investment options.

A current example is the planned pension investment account. For a large number of existing Riester contracts, it raises the question of whether they should be continued or supplemented by other forms of provision. For a sound assessment, advisors need an overview of existing contracts and portfolios.

André Rabenstein describes how AI can support product selection, documentation, and follow-up in addition to meeting preparation. The share of working time spent on the actual consultation increases accordingly.

What This Means for Banks

The technical requirements are in place. The limiting factor is often integration. Fintechs and neobrokers often integrate new interfaces in short cycles. In contrast, André Rabenstein observes decision-making processes at traditional banks that can span several years.

This has consequences for the customer relationship. Clients no longer compare banks solely on fees and interest rates, but also on whether they can manage their finances across different applications. Institutions that do not integrate data from external sources see less of their clients’ financial situation and thus have fewer starting points for advice.

The planned EU regulation FiDA (Financial Data Access) aims to create a uniform framework for this data exchange. It regulates access to financial data beyond payment accounts, such as investment and insurance data. In the podcast, André Rabenstein identifies European data standards as a prerequisite for AI-powered financial services.

The Role of Advisors

When AI analyzes data and prepares recommendations, the role of the advisor shifts. André Rabenstein sees their future task primarily in personal contact: building trust, contextualizing complex decisions, and explaining recommendations clearly. Additionally, there is a supervisory function, as long as AI systems can misinterpret information or hallucinate content.

The conversation also touches on data protection, liability, and regulation. Very personal information can be derived from transaction data. It remains to be seen which data customers are willing to share, who is liable for incorrect recommendations, and how the transfer of sensitive data is controlled. For regulated account information service providers like wealthAPI, explicit user consent is a prerequisite for every data retrieval.

Conclusion

André Rabenstein’s prediction in the podcast: AI will prevail in financial advisory. What remains open is the pace at which this happens and how many advisors integrate the technology into their customer relationships. From wealthAPI’s perspective, the quality of AI-supported advice depends less on the model than on the data basis. Recommendations are only as robust as the completeness and quality of the underlying wealth data.

The full episode is available at Payment & Banking.