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Key findings
- Markets experienced the worst year since 2008, with the MSCI World Index down by 18% in 2022 due to a rising level of uncertainty, inflation, fears of recession, and central bank rate hikes
- On average, assets under management fell by 11%, revenues by 15%, and profits by 16% in 2022
- Several options available to address profitability pressure and reduce costs, such as IT optimization, downsizing, or streamlining the portfolio of offerings
- At the same time, generative AI could reduce costs for asset managers by up to 15% in the medium term
Growth of largest and selected other asset managers
Overall, US asset managers (AMs) grow significantly faster on average than their European counterparts. European AMs achieved an average asset under management (AuM) growth rate of 36%, vs. 56% achieved by their US peers.
Financial performance comparison
The asset management industry took a big hit in 2022. AuM, revenues and profits declined, with alternative asset managers bucking the trend. The downturn in AuM for asset managers in 2022 is in line with declining markets and muted net flows.
Profitability is struggling to keep pace with market recovery
Despite improving AuM levels and revenue growth, profits are declining in a recovering market environment – asset managers will need to exercise cost control in their business portfolio, organizational model and operations.
Strategic cost considerations for asset managers
To counteract the profitability pressure, AMs have different options for optimizing their cost structures.
GenAI in asset management
The vast potential of GenAI in asset management can be attributed to a small number of well-defined application fields with the potential to increase efficiency, quality and revenue. Looking at the GenAI cost efficiency potential in asset management, the technology is expected to cut total costs along the AM value chain by 5-15%.
In order to bring GenAI to life, the following six organizational and technical requirements need to be met:
- 1AI organziational and operating model
- Embed GenAI in the AM organization (decentralized vs. central organization)
- Integrate GenAI into the asset management operating model
- 2Risk, regulation and compliance
- Adhere to relevant regulations (e.g., EU AI Act)
- Consider AI-relevant risks (e.g., reasoning behind and traceability of AI-enabled investment decisions)
- 3Cloud transformation
- Leverage GenAI as a driver of cloud transformation, due to the high data volumes and computing power
- 4“Sandbox” approach
- Realize quick wins via GenAI pilots in secure cloud environments
- 5Data, infrastructure and security
- Ensure data availability, quality and transparency, having regard to data privacy policies
- 6Skills, mindset and change
- Ensure technical and end-user skills and capabilities (upskilling)
- Achieve "data-driven" mindset change and effective change management
Martin Rietzel, Sandro Kanzian, Julia Burger and Katrin Wagner also contributed to this report.
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