1. “Upgrading Technology Financing: ML Enabling a Data-Powered Process”
Arturo Moreno
CEO at Preseries

2. Introductions & Outline
Who are you, Who am I and What this is about…
About me: I have the vision of a fast, efficient and fair fundraising process so that innovation and entrepreneurship can flourish freely
About you: People working on venture? Corporate Venture? Thoughts about VC?
Outline:
What is Venture Capital? How is it working today? Shall we improve it at all?
What makes a problem addressable with ML? Is this the case?
PreSeries: What we have done, what we are working on

3. How Venture Investing works
Venture capitalist wear multiple hats (time-constrained)
Souce: Harvard Business Review

4. What’s the criteria to invest?
What do you prefer, a great team in a mild market or the other way around?

5. What is the performance?
Only 10% of the events produce a meaningful return
Souce: Correlation Ventures and Pitchbook analysis

6. So… Can software be helpful?
VCs have a qualitative approach to most of these tasks

7. Can ML “solve” Venture Capital?
Machine Learning 101 (My apologies to the BigML crew ;))
ML, at its core, is a set of statistical methods meant to find patterns of predictability in datasets  Good! I like Math…
Great at determining how certain features of the data are related to the outcomes we are interested in  Mmm… related, what do you mean?
ML algorithms cannot access any knowledge outside of the data we provide  Well, who does right?

8. Can ML “solve” Venture Capital?
Machine Learning 101 (My apologies to the BigML crew ;))
Ok, so ML works well for situations where:
We want a prediction rather than causal inference  Do I really need to know why? Did I ever knew? And the why of the why?
The problem is sufficiently self-contained, or relatively insulated from outside influences  I will build the best possible dataset!

9. Can ML “solve” Venture Capital?
The check-list
Let’s define “solve”: venture capital can be broken down in multiple problems and beyond identifying unicorns… (prioritize dealflow, find companies that will reach certain milestones, find closest competitors, additional investors or acquirers, monitoring and optimization of internal metrics,…)
Intuition is needed (ensuring self-containing, feature engineering, model customization and making sense of the results)
Do we need then ML to do a better job at financing technology?

10. Can ML “solve” Venture Capital?
My prediction
Machine Learning CANNOT “solve” today for Venture Capital, yet a data-centric culture at investing organizations (and consequently at startups) will bring benefits to investors both in terms of performance and productivity, through a reduction of decision fatigue.
Investors will be able to spend more time working with their portfolio companies, supporting founders in new meaningful ways.

12. ML widely adopted in investing

13. VCs slowly adopting data

14. What is the cost of using ML?
In-house solutions represent a big time and money effort

15. What can I get help with?
Data collection and processing are time-consuming tasks

16. The Operating System for the startup investing community

17. PreSeries Tech Architecture

18. The 21st century dashboard

19. Preseries Public Profile
Airbnb Profile

20. The PreSeries Startup Battles
How VC will work in (quite some) years

21. Towards self-containment…
Is publicly available data enough to benefit from Machine Learning applied to venture capital?

22. Preseries OS
PreSeries OS ensures relevant, time-efficient and confidential relationships between investors and startups for the exchange of information

23. Revolutionary DBs for venture

24. Allowing a explosion in ML

25. To visualize new insights

26. Coming soon… Want to be part of the future of VC? Get in touch