Wealth managers spend an average of 40% of their time on reporting and administration. Time that does not go to client relationships, not to investment advice, not to acquisition.
AI changes that fundamentally. In this article we show you how.
The reporting problem
Where does the time go?
A typical quarterly reporting cycle:
| Activity | Time (per 100 clients) |
|---|---|
| Collecting data from systems | 8 hours |
| Performance calculations | 12 hours |
| Writing commentary | 16 hours |
| Formatting and layout | 8 hours |
| Review and corrections | 6 hours |
| Total | 50+ hours |
That is more than a week of work. Every quarter. On repetitive tasks that add little value.
The real problem
It is not only about time. It is about:
- Quality: manual work means mistakes
- Consistency: everyone on the team does it differently
- Scalability: more clients means more FTE
- Focus: senior people doing junior work
The AI reporting solution
How it works
Our AI reporting agent automates the whole process:
Step 1: pull the data
- Automatic connection to custody systems
- Real-time portfolio data
- Benchmark data
- Market data
Step 2: analysis
- Performance calculation (TWR, MWR)
- Attribution analysis
- Risk metrics (volatility, drawdown, VaR)
- Benchmark comparison
Step 3: content generation
- Personalised market commentary
- Portfolio-specific analysis
- Explanation of performance
- Outlook and advice
Step 4: output
- Branded PDF in your house style
- Interactive dashboard
- Ready to email
- Archived for compliance
Results
| Metric | Before | After | Improvement |
|---|---|---|---|
| Time per cycle | 50 hours | 5 hours | 90% |
| Errors | 5-10 per cycle | 0 | 100% |
| Turnaround time | 2 weeks | 2 days | 85% |
| Cost | €8,000 per cycle | €800 per cycle | 90% |
ROI calculation
Assumptions
- 100 clients
- €50M AUM
- 4 quarterly reports per year
- Average fee 0.8%
- Hourly cost per employee: €80
Cost of the AI solution
| Component | Cost |
|---|---|
| One-off setup | €15,000 |
| Monthly cost | €500 |
| Year 1 total | €21,000 |
Savings
| Item | Calculation | Saving |
|---|---|---|
| Time | 45 hours × 4 × €80 | €14,400 per year |
| Correcting errors | 10 hours × 4 × €80 | €3,200 per year |
| Faster invoicing | 0.5% × €400,000 | €2,000 per year |
| Total saving | €19,600 per year |
ROI
- Year 1: close to break-even (€21,000 in costs against €19,600 in savings)
- Year 2 and beyond: €13,600 net saving per year (only €6,000 in annual costs)
- Payback period: 13 months
A case from practice
The situation
A wealth manager with:
- 75 clients
- €1.5 billion AUM
- 2 FTE working on reporting
- Reports often late
Our approach
- Week 1-2: analysis of the current process and systems
- Week 3-4: AI agent configuration and integrations
- Week 5-6: testing with 10 pilot clients
- Week 7-8: training and full rollout
Results after 6 months
- Reports ready 3 days earlier
- 1 FTE freed up for client contact
- NPS score up from 42 to 58
- New clients won thanks to faster reporting
Compliance
AFM requirements
Our solution is designed with AFM compliance in mind:
- Transparency: disclosure that reports are AI-supported
- Human review: always a check before anything is sent
- Audit trail: complete logging of every calculation
- Archiving: 7 years of retention under MiFID II
Quality assurance
- Automatic consistency checks
- Comparison with the previous period
- Alerts on deviations
- Multi-level approval workflow
Getting started
Step 1: assessment
We start with a free assessment:
- Which systems do you use?
- What does your reporting process look like?
- Where are the quick wins?
Step 2: proof of concept
We build a working demo with your data (anonymised):
- Reports for 3 example clients
- In your house style
- With your KPIs
Step 3: implementation
Once you give the green light we roll it out:
- Full integration with your systems
- Training for your team
- Support through the first quarterly cycle
Read more
Or book a call straight away to talk through your situation.
