It's Monday morning. A consultant opens his Outlook, two emails, three callbacks, a new client. He scrolls through last week's BMF letter, which is hardly any different from the one before last. On his desk: a mixture of investment files, inheritance issues and pension notices. The business of independent financial advice is a business of details. And of hours. And of responsibility. Between legal obligations and personal recommendations, a field of tension arises that cannot simply be "automated". But it can be structured. And this is exactly where the quiet but precise use of artificial intelligence - or rather: machine thinking assistance - begins.
1. what a financial advisor actually does - according to the scale of fees
The Federal Financial Supervisory Authority (BaFin) has clear boundaries. Anyone offering financial advice on a commercial basis is operating in a regulated field. It is subdivided into product categories: Insurance brokers, fee-based financial investment advisors, real estate loan brokers. The fee structure is often based on fee-based advice (e.g. §34h GewO), in which advisors charge directly for their time and expertise. What is often overlooked is the variety of actual activities. They range from:
- Status analysis:
Understanding, structuring and explaining the balance sheet, liquidity plan and tax assessment notices. - Target definition:
Retirement provision, asset accumulation, protection, succession, intergenerational contract. - Product comparison:
ETFs vs. funds, Riester vs. basic pension, fixed-term deposits vs. bonds. - Cost analysis:
TER, issue surcharges, management costs, tax advantages. - Recommendation formulation:
individual, liable, traceable. - Accompanying documentation:
Consulting protocols, risk disclosure, market observation.
These processes are not always linear. They have to be documented iteratively, customized and audit-proof. This is where the real work lies. So it's no wonder that many consultants spend more time with paragraphs than with people.
2. where time is lost - and how machines can give it back
In our work with fee-based advisors, broker pools and bank branches, we see every day where time is wasted. It's not the conversation with the client that holds up. It is:
- Excel tables with outdated formulas
- Poor quality scans that have to be manually post-processed
- constantly changing forms (e.g. AVAD, custody account transfers, basic information sheets)
- E-mail inboxes as a CRM replacement
- Unclear notes on the content of conversations that no one but the author understands
This is not a science fiction solution, but something much more down-to-earth: structured text recognition, automatic classification and adaptive recommendation systems. Not a robot voice, but a learning filing cabinet.
3. from form chaos to case structure - a concrete workflow
Let's take a typical advisory case: a 52-year-old client wants to "get things in order", has three bank deposits, two life insurance policies and an inheritance in prospect. The advisor receives:
- Bank statements or Securities settlements
- Insurance policies or premium invoices
- a living will in paper form
- Tax assessment notices 2021 to 2023
- an informal letter from the aunt regarding the gift
Today: The consultant scans, prints, marks and transfers. Ideally, he enters notes into his self-built Excel matrix.
With AI support
The consultant pulls the documents into a system that:
- automatically recognizes which document is involved (e.g. tax assessment notice vs. securities account overview)
- Relevant amounts and data extracted (allowances, terms, income)
- enters these into a case file in a structured manner
- differences between the years
- and forms are automatically prepared for the interview
People do not need to be replaced. They are strengthened. Time that is not spent on organizing the documents can be invested in interpretation and discussion.
4. how much advice can be standardized?
Of course, no algorithm knows the client's family history. But it can help the advisor has more time to hear exactly this story. Financial advice is all about trust. And trust takes time. When we build systems today that pre-sort incoming mail, pre-fill comparison tables and suggest formulations for advisory documentation, it's not about simplification - it's about precision.
There is no law that requires an advisor to manually type amounts into five columns. But there are obligations to document them correctly. This is exactly where technology Do not replacebut can secure it.
5 What does not work - and should not work
What remains is the responsibility. No system will (or should) assume liability. No software can replace the individual assessment of a divorce, the inheritance of a family business or the ethical question of whether to invest in military assets.
But: an advisor who doesn't have to go through the files is more present. A system that prepares five possible fund comparisons gives room for questions that would otherwise go unanswered. And documentation that is legible, comprehensible and complete not only protects the advisor - it also creates trust with the client.
6. the biggest levers in day-to-day business
Anyone working as a consultant today should look for automation where there is no personal touch anyway. Examples:
- Document recognition and classification:
No scrolling through PDFs, but automatic sorting by type (e.g. "BU insurance with dynamic" vs. "endowment insurance") - Audit:
Preliminary review of fees or brokerage statements for anomalies - Consultation protocols:
Suggestions for formulations based on previous discussions - Forms management:
Automatic filling of securities account transfers, bank forms, advisory documentation - Deadline reminders and deadline control:
Especially for policy expiration dates or revocation periods
7 The real change: less technology, more attitude
Technology becomes a tool. Not a promise. At Helm & Nagel, we don't see the future of financial advice in "AI-supported complete advice", but in a stronger advisor who can use their time more efficiently. Who concentrate on conversations, on the realities of life, on goals. And who knows that a machine-based analysis is no substitute for the moment when a client talks about their will for the first time.
Conclusion: More order, less oracle
Financial advice is not fortune-telling, but a craft. It requires an overview, knowledge and concentration. Machines can provide support, structure and security. But they remain what they are: Tools. They help, but they do not decide.
And that's a good thing.
Because in the end, it's not the cleanest Excel file that counts, but the connection between two people. The task of technology is not to replace this - but to create space for it to happen more often.
Excursus: How much AI is already in financial advice - and how differently is it being used?
The actual use and acceptance of AI in financial advice differs considerably depending on the customer group - not only in terms of attitude, but also in the way in which AI-based services are actually used.
Younger, digitally savvy customers use AI-supported tools primarily for self-information and quick orientation. Chatbotsinteractive budget apps or automated loan calculators are seen as welcome support here. In this group, the proportion of users who already regularly use AI-supported financial services is estimated at 25 to 30 percent - and the trend is rising. The simple integration into mobile banking apps and 24/7 access create additional benefits.
Higher-income households use AI primarily for individual recommendations when making lending or investment decisions. Here, technology is not only seen as a convenient addition to traditional advice, but also increasingly as a quality feature for financial services. Factors such as objective risk assessment, personalized options and scenario simulations play a central role for this target group.
In contrast, older and lower-income customer groups a much more reserved attitude towards AI. Here, use is generally limited to passive functions, for example in the form of automated notifications or standardized suggestions in customer portals. The study confirms this: Many in this group do not yet see the added value - or have reservations that the technology could replace human proximity and individual advice.
The study shows: "AI in financial advice"1 does not mean the same thing for all target groups. While some customers are already actively using AI-based decision support, others are more hesitant or dismissive of the technology. For banks and financial service providers, this results in a twofold task: using technology intelligently and for specific segments - and at the same time building trust through transparency.
- TeamBank. (2024, September 11). Study: Young people are more open to AI-supported financial advice - TeamBank. https://www.teambank.de/medien/presse/studie-junge-menschen-sind-aufgeschlossener-fuer-ki-gestuetzte-finanzberatung/ ︎
EN 
DE 
ES 
FR