The asset management vertical has the potential to nurture significant venture-backed outcomes in the coming years.

Conditions are ripe for a broad technical re-platforming in this sector, still being largely served by monolithic front-to-back technology providers and outsourced asset services firms. Cost-income ratios have continued to rise as asset managers face fee and cost pressures. Customers are demanding differentiated products and increasingly personalisation. And the value creation to be unlocked through the promise of market consolidation still requires success in complex post-merger technical integrations.

As a result, we are starting to see a shift away from front-to-back platforms and siloed application-specific data architectures towards a more composable technology stack founded on open data ecosystems and cloud-native infrastructure. This is allowing enterprise managers to more easily work with best-of-breed software across asset classes and functions, reduce their cost-to-serve, and more rapidly innovate at a product level.

As the category is unbundled, we see a particular depth of opportunities in several areas:

• Vertically-focused data management platforms: providers such as Finbourne are providing much more flexible infrastructure to support front, middle and back office teams with a reliable, single-source-of-truth for internal and external data. We believe that that complexity of the data landscape and time-to-value in this sector does demands a domain-specific approach for the greatest success.
  
• Automation of middle and back-office operations: improved data architectures and AI will support automation and optimisation of post-trade operations that currently are plagued by manual processes and cost inefficiencies. For example, Access Fintech - a Dawn portfolio company – has created a shared data ecosystem for buy-side, sell-side, asset servicers and OMS platforms that is automating the resolution and settlement of trade exceptions for some of the world’s largest asset managers.
• AI co-pilots for investment research and risk: financial analysts are an obvious persona of knowledge worker that could benefit an AI co-pilot. We’ve started to see emergence of AI tools like Hebbia that help automate the investment research process, helping analysts to combine, gather and generate insights from vast sets of unstructured and structured data, as well as AI-led UX within asset-specific data platforms such as 9fin
• Digital assets infrastructure: we are just at the start of the movement of traditional financial assets onto digital rails and expect this to accelerate significantly in the coming years as the regulatory outlook settles. There will be a need for a new set of digital-native providers – like Copper and Elwood in Dawn’s portfolio - that enable the efficient 24/7/365 trading and settlement of these assets on-chain
• Private assets ecosystem: despite significant growth over the last decade, private markets are still significantly underserved by modern technology solution. GPs and service providers still often manage core operations - from valuations to reporting - in spreadsheets and unstructured PDFs leading to significant manual processing. We’ve seen a wave of startups such as Vega, Pactio and 73Strings emerge addressing exactly these challenges. AI will be key to unlocking the data challenges in this space.