FAQ Section | Frequently Asked Questions

(Fin)Influencers combine three roles: 

• Opinion builder 
• Mediator 
• Sales lever 

This is often not recognizable to outsiders: 

• whether financial incentives exist 
• whether commissions flow 
• whether reach is monetized 
• whether vested interests are at play 

Their effect is particularly strong because: 

• Trust is created through personality 
• Complexity is emotionally reduced 
• Risks are rarely addressed structurally 

This creates a double asymmetry: knowledge advantage Influence through authority

Numbers, curves and rankings create an objective impression. They appear neutral - even if they are not. 

In combination with: 

• reach 
• storytelling 
• success stories 

a strong activation narrative emerges: "Others are doing it too - so it can't be wrong." 

This dynamic reinforces asymmetries because: 

• context is missing 
• risks are not experienced 
• responsibility remains diffuse

1. actor clarity 

Is it clear who is behind the offer? 

☐ Legal entity clearly named 

☐ Responsible persons identifiable 

☐ Roles (platform, intermediary, provider) clearly separated 

☐ Use of anonymous or diffuse structures 

Classification: Unclear actor structures make any well-founded classification of interests difficult. 

2. business model & monetization 

Is it clear how the provider earns money? 

☐ Fees transparently disclosed 

☐ Commissions disclosed 

☐ Performance-based remuneration clearly explained 

☐ Monetization only implicit or hidden 

Classification: If you don't understand the business model, you can't evaluate incentives 

3. Incentive structure 

What is the economic benefit for the provider? 

☐ Long-term user success 

☐ Activity / trading frequency 

☐ Subscriptions / signals / volume 

☐ Unclear or contradictory 

Classification: The more activity is rewarded, the greater the potential conflict with user interest. 

4. Conflicts of interest Are potential conflicts of interest openly disclosed? 

☐ Proprietary trading of the provider disclosed 

☐ Participations / partnerships transparent 

☐ Third-party commissions clearly marked 

☐ No or only general information Classification: It is not the conflict that is problematic - but its invisibility. 

5. Origin of results & representations 

Is it clear where performance data comes from? 

☐ Real money, demo, backtest clearly separated 

☐ Time periods traceable 

☐ Cancellations / restarts visible 

☐ Results without context 

Classification: Figures without origin are marketing, not information 

6. Presentation of risks & losses 

Are risks communicated realistically 

☐ Drawdowns visible 

☐ Loss phases explained 

☐ Volatility named 

☐ Focus almost exclusively on profits 

Classification: Transparency is more evident in losses than in successes. 

7. Role of influencers & third parties 

Is it clear what role external actors play? 

☐ Advertising clearly marked 

☐ Remuneration disclosed 

☐ Separation of opinion and marketing 

☐ Emotionalized success narratives 

Classification: Reach does not replace neutrality 

8. Control & freedom of choice 

How much control remains with the user? 

☐ Clear intervention options 

☐ Risks individually adjustable 

☐ Automation optional 

☐ Loss of control by design 

Classification: Transparency also includes the question of who stops in case of doubt 

9. Language & Tonality How is communication done? 

☐ Factual, explanatory 

☐ Differentiated 

☐ Restrictions clearly named 

☐ Simplifying, emotionalizing 

Classification: The more emotionalized the communication, the more important critical distance is 

10. Expectation management 

How are results classified 

☐ Marked as historical 

☐ Marked as non-transferable

☐ Without promises of success 

☐ Implicit assumptions of repetition 

Classification: Transparency is shown by what is not promised.

How do you recognize problematic lack of transparency 

1. Unclear actors 

• Provider only appears as a brand or platform 
• Persons responsible remain in the background
• Legal structure difficult to find or convoluted 

Warning signal: Responsibility cannot be clearly assigned 

2. Non-transparent monetization 

• It is unclear how money is earned 
• Revenues are only described in general terms ("partnerships", "community") 
• Commissions or profit sharing are missing or relativized 

Warning signal: Interests cannot be evaluated 

3. Performance without origin 

• Track records without clear labeling (real money / demo / backtest) 
• Smooth curves without recognizable loss phases 
• Periods selectively chosen or not explained 

Warning signal: Numbers appear objective, but are without context 

4. Activation instead of classification 

• Strong focus on "Start now", "Don't miss out", "Others are already benefiting" 
• Gamified elements replace explanation 
• Complexity is reduced emotionally instead of objectively 

Warning signal: Behavior is triggered, not reflected upon. 

5. Demo results as proof of performance 

• Demo or simulated strategies are in fact treated like real money 
• Indications of a lack of capital commitment are hidden or subordinate 
• Transferability is implicitly suggested 

Warning signal: Reality is replaced by simulation.

6. Black box strategies 

• Decisions are not explainable, only observable 
• Rule changes are made without announcement 
• Losses are dismissed as a "market phase" 

Warning signal: Control is relinquished without insight 

7. (Fin)Influencer without disclosure 

• Success stories without clear labeling of advertising 
• Compensation or affiliate models not transparent 
• Personal brand replaces factual classification 

Warning signal: Trust is generated emotionally, not based on content. 

8. Results-oriented language 

Focus almost exclusively on profits 

Risks only mentioned in general or formal terms 

Drawdowns are trivialized or ignored 

Warning signal: Expectation management is missing. 

9. Responsibility 

• is shifted losses 
• are attributed to the user successes 
• implicitly to the system or provider 
• clear responsibilities are missing 

Warning signal: Asymmetrical risk allocation 

10. "Too simple to be true" 

• Complex processes are presented as trivial 
• Time expenditure, learning curve or stress are negated 
• Automation is equated with safety 

Warning signal: Reality is systematically ignored.

The less visible it is who decides, who benefits and who bears the risks, the greater the information asymmetry.

1. affiliate model

Compensation per referral or completion 

How does it work? 

The finfluencer receives compensation when users use a personalized link to: 

• open an account 
• subscribe 
• register a product 

Payment is typically made: 

• per click (CPC) 
• per registration (CPA) 
• or per subscription 

Why is this attractive? 

Easy to implement Scalable with reach Independent of users' actual trading success 

Where is the conflict of interest? 

The economic incentive lies before the actual use: Motivation for activation, not for suitability testing Success of the model does not depend on whether users benefit in the long term 

Critical classification 

Affiliate models reward persuasiveness, not quality of results. The more emotional and simplified the presentation, the higher the conversion 

2. Commission model on current sales 

Compensation dependent on activity or volume 

How does it work? 

The finfluencer receives: 

• a share of trading fees 
• or a volume-based commission 

As long as the referred user is actively trading, compensation flows. 

Why is this attractive  

• Recurring income 
• Long-term monetization of the community 
• Strong leverage with active users 

Where is the conflict of interest? 

A direct conflict of interest arises here: 

• User interest: Act selectively or pause 
• Supplier & influencer interest: Trigger action 

Inactivity is often rational from a user perspective - but economically disadvantageous from a commission perspective. 

Critical classification 

This model favors: frequent action permanent market presence low inhibition thresholds Not because it delivers better results, but because activity itself is monetized. 

3. "Order book share" / flow-based participation 

Compensation via order flow or indirect market effects 

How does it work? This model is less visible, but structurally relevant. 

The finfluencer benefits indirectly when: 

• Order flow is bundled 
• Trading volume is directed to certain counterparties 
• Liquidity or spread effects arise 

The remuneration is not always explicitly per user, but via: 

• revenue sharing 
• institutional agreements 
• volume-based rebates 

Why is this attractive? 

• High scaling with a large reach 
• No direct dependence on user success 
• Often low transparency obligation 

Where is the conflict of interest? 

The user believes they are following a strategy or opinion. In fact, their order behavior is economically exploited without: 

• decision quality 
• result 
• or risk profile 

playing a role. 

Critical classification 

This model shifts the focus completely: from the outcome of the user to the usability of their behavior This mechanism is usually not recognizable to outsiders.

These constellations mentioned here in relation to the respective models are not illegal and not problematic per se

They become problematic when:

• they are not made transparent
• they are perceived as a neutral recommendation
• conflicts of interest are not recognizable

The user judges content according to its message -
the provider is remunerated according to its effect. This is the core of information asymmetry.

Rule of thumb: If remuneration is linked to activation, volume or order flow, every recommendation should also be read as a business model.

Conflict of interest is mitigated if the decision-making process of a prospective customer 

• is carried out without direct influence
• no dark patterns are applied
• information asymmetries are prevented through targeted clarification or warnings
• no one-sided presentation and omission of information
• is mirrored: sufficient context is provided to make informed and, above all, risk-based decisions independently

Why is it important to recognize commission models? 

Commission models influence how content is created and how it is presented. If you know the economic incentives behind recommendations, you can better classify statements - regardless of whether the recommendation is well-intentioned or technically correct. 

It's not about discrediting content, but about making interests visible. 

How can you recognize affiliate models? 

Affiliate models can often be recognized by the following characteristics: 

• Links with tracking parameters 
• Notes such as "partner link", "advertising link", "affiliate" (often placed inconspicuously) 
• Emphasis on simple entry points ("Ready to start in a few minutes") 
• Focus on registration or account opening 

Typical is: 
The recommendation ends before the actual use. 

Classification: The economic incentive lies in the conclusion - not in long-term success. 

How can you recognize volume- or activity-based commissions? 

These models are less obvious, but are reflected in the tone and frequency of the content:

• regular calls to action 
• permanent market presence ("There's movement now") 
• little thematization of pauses or non-action 
• emphasis on opportunities instead of weighing up decisions 
• Activity is often presented as the normal state. 

Classification: If inactivity hardly ever occurs, activity is usually economically relevant 

How to recognize order book or flow-based investments 

These models are the most difficult for users to recognize, as they are rarely communicated openly. Indirect indications can be: 

• strong bundling on a few platforms or products 
• emphasis on "liquidity", "tight spreads", "optimal execution" 
• upgrading of high trading frequency 
• hardly any discussion of alternatives 

The recommendation appears neutral, but is structurally bound. 

Classification: It is not the result that is monetized here, but the behaviour 

What role does the labelling of advertising play? 

Formal labels such as: 

• "Advertisement" 
• "Advertising" 
• "Sponsored" 

are legally relevant, but often not very informative in terms of content. 

The decisive factor is not the presence of advertising, but: 

• how strongly it shapes the narrative 
• whether alternatives are mentioned 
• whether risks are treated equally 

Classification: Transparency does not start with the label, but with the classification. 

Why are success stories a strong indicator? 

Success stories fulfill several functions: 

• emotional activation social affirmation 
• simplification of complex contexts 

They are particularly effective when: 

• the way there is presented in a shortened form 
• there is a lack of setbacks 
• transferability is implicitly assumed 

Classification: Where storytelling replaces analysis, there is usually a monetary incentive. 

How do you recognize a lack of alignment of interests? 

A central test criterion is the question: Does the recommender bear the same risk as the following person? 

Warning signals: 

No clear statement on own capital commitment 

Focus on reach instead of responsibility 

Missing statements on own loss phases 

Classification: If risk equality is missing, there is structural asymmetry. 

Are there simple test questions for users? 

Yes. Three simple questions are often enough: 

How does the recommender earn money? 

What will they actually benefit from if I take action? 

What would happen if I did nothing? 

The clearer these questions can be answered, the less information asymmetry there is 

Final classification 

Not every remunerated recommendation is problematic. In certain constellations, remuneration models can be a legitimate and necessary refinancing instrument for developing new products, services or initiatives. In particular, where these funds serve to educate, impart knowledge or develop consumer protection tools, they fulfill a comprehensible function. The decisive factor here is not the existence of economic interests, but the motivation with which they are used - and the purpose they ultimately serve.