3 AI use cases for investors and start-ups: How artificial intelligence is changing the world of venture capital.

The venture capital industry operates in a dynamic environment in which quick decisions and precise predictions often determine the success or failure of an investment. Uncertainty is particularly high in the case of early investments in start-ups, as there is often little data that investors can rely on. This is where artificial intelligence (AI) can help: Through data-driven models and advanced algorithms, it opens up completely new opportunities for investors and startups. Machine learning (ML) and other AI technologies make it possible to gain valuable insights from large amounts of data, predict future developments and better manage risks.

In the following, three use cases are presented on how AI is revolutionizing the investment process and the growth of startups: predicting the success of startups, predicting post-money valuation and sector cluster analysis with Latent Dirichlet Allocation (LDA).

1. Predicting the success of start-ups

Investors face the challenge of recognizing early on whether a startup will be successful. AI models such as Random Forests and Gradient Tree Boosting (GTB) analyze historical data to predict the future success of a company, be it through an acquisition, an initial public offering (IPO) or further financing rounds.

The data used includes, for example:

• Date the startup was founded: older startups may have higher chances of survival.

• Founders' experience: Number of founders, their professional networks and their education.

• Previous financing rounds: Number and amount of previous investments.

These AI-supported predictions help investors to make informed decisions and reduce the risk of bad investments. One disadvantage, however, is that the quality of the predictions depends heavily on the availability and accuracy of the data. Very young start-ups in particular often lack comprehensive information, which can affect the models.

Performance: Studies show that the prediction models achieve an accuracy of around 82%, with Gradient Tree Boosting (GTB) in particular impressing with its high precision. For successful financing rounds in particular, the accuracy is between 64% and 68%, which is a significant improvement on human experts.

2. Prediction of the post-money valuation of start-ups

The post-money valuation is a decisive factor for investors in determining the value of a startup after a financing round. With the help of algorithms such as ElasticNet and XGBoost, valuations can be predicted based on various variables.

Data used for these models include, for example:

• Investment amount in previous rounds: The amount of capital invested.

• Number of investors: More investors can mean a higher valuation.

• Market segment: Which industry or sector influences the value of a company.

These predictions allow both investors and start-ups to create a more realistic basis for negotiation. The advantage lies in the improved accuracy of the valuation, which reduces the risk of over- or undervaluations. One challenge, however, is that market conditions can change quickly and are often not fully captured by the models.

Performance: XGBoost post-money valuation prediction models achieve around 90% accuracy, which is impressive precision for valuation predictions. These models enable investors to conduct more realistic and informed negotiations.

3. Sector cluster analysis with Latent Dirichlet Allocation (LDA)

Investors use sector cluster analysis to efficiently categorize startups by industry. By using Latent Dirichlet Allocation (LDA), an algorithm from natural language processing, companies can be automatically assigned to thematic clusters based on their descriptions.

Examples of data used are:

• Product and company descriptions: Text data from company profiles or press releases.

• Keywords used: Terms that frequently occur in the description of companies and lead to classification in certain sectors.

This automatic sector analysis saves time and provides investors with a better overview of market trends and new sectors. However, implementing LDA is challenging as it requires well-structured data and clear mapping, which can be difficult for companies with a broader focus.

AI offers enormous potential for optimizing the investment process and helping start-ups to grow better and faster. The use cases presented show how investors can make more precise decisions by using machine learning and other AI technologies. However, it should be noted that the models have their limitations, especially when it comes to the availability and quality of data. Nevertheless, AI will have a lasting impact on the future of investments and start-ups.

This article is based on several scientific papers. For more detailed information, these are listed here:

• Arroyo, J., et al. (2019). Assessment of machine learning performance for decision support in venture capital investments. Ieee Access, 7, 124233-124243.

• Bento, F. (2017). Predicting start-up success with machine learning. (Master's thesis, Universidade NOVA de Lisboa (Portugal)).

• Ang, Y. Q., et al. (2022). Using machine learning to demystify startups’ funding, post-money valuation, and success. Springer International Publishing, 271-296.