How is AI reshaping the venture capital industry? | Tangled AI
#AI has shown the world what investing in the right technology can achieve. It has been able to induce a new level of sophistication in almost every operation it is used for. Every division of #artificialintelligence viz. natural language processing, machine learning, computer vision, and robotics have their own set of use cases and have been displaying a world-wide acceptance in industries like banking, healthcare, space, military, or even education. Data in real-time, boosted by the growth of digital commodities is the core component of AI. AI systems are trained through these enormous chunks of datasets. Today we have reached a stage where we can call our economy the ‘digital economy’, thanks to thousands of technologies that are working round the clock to boost it.
To power this digital economy even further, AI has begun to spread its wings across stock markets and the venture capitalist industry. Since data is the heart of investing, a professional investor like a venture capital would always place his money on decisions that are backed by data. These data-driven decisions can be generated by feeding the right datasets and training the AI systems. Remember, even though there are several metrics already established in the venture capital industry, none of them are as dynamic and data-driven as those set by AI systems.
As expected, the venture capitalists are gradually seeking and resorting to AI for making better decisions and generating an optimized investment process. In this post, let us look at the different ways in which AI is helping in reshaping the venture capital industry -
AI as a virtual investment aid
According to a study, there is a high risk in funding first-time entrepreneurs or startups as their probability of success is just 18%. This often prevents venture capitalists from investing in startup companies. Gut instinct cannot always be right at all times. Luckily, a venture capital business armed with an AI system can easily identify companies that have the potential to manifest themselves into a successful organization in the long run. AI systems with the right algorithm embedded in them act as your personal decision-maker in predicting the probability of success of a company based on past industry happenings, revenue growth, market size, and other factors.
Fly Ventures, a Berlin-based venture capital business uses AI models along with #machinelearning algorithms to target European startups that are worthy of seed funding. The company’s AI algorithm is capable of generating information about 1000 startup companies a week. Such AI products help venture capitalists to find and invest in the right companies when the right metrics are satisfied.
Clearbanc is another venture capital that uses AI extensively in determining the funding terms. The focus mainly on unit economics and repayment through the revenue sharing model. This is an easier way to give unrestricted access to the capital to boost their development.
Keeping track of investors
Some AI products also aid first-time entrepreneurs to keep track of venture capitalists and the investment market trends based on which the VCs provide better funding. By garnering the power of AI, it arms these entrepreneurs with all the data and insights about the past investing pattern of the venture capitalists. This technology does not disrupt the business operations, rather defines a better standard for it. By using AI in your startup, you know that you are on the right path when a VC calls you out of the blue just because you fit in their desired persona which has been chiseled by your AI system.
Hone Capital, a tech investment firm has created a database comprising about 300,000 deals since the past decade to feed to an ML framework. This is constantly being reviewed, updated, and has identified about 400 characteristics, and out of them, 20 characteristics are recognized for better success in pitching to venture capitalists.
Staying updated
AI algorithms are primarily self-learning and are capable of learning from their previous doings and patterns. These patterns help to reveal the growth factor of a company in the near future. This in turn helps venture capitalists to stay updated in their funding expedition searching for new startups to invest in. Startups are sprouting at every nook and corner of the world. To keep a track of such a pacy ecosystem, investors have to seek the help of AI systems that can provide better insights to them which are updated to the most recent event in the industry. Entrepreneurs can also use the real-time AI databases to continually update their portfolios according to the current market conditions and get funding by adapting to new technologies preferred by the investors.
For example, Beacon, an AI platform is developed exclusively for venture capitalists to stay updated on emerging companies by keeping tabs on the performance of about 6 million companies with the help of about 10 million data sources on patent registrations, open-source contributions, and performances.
Reliable segregation of data
#AImodels powered by several clustering algorithms help to organize datasets to avoid overlapping and complexity. This is especially true in a venture capital industry, where tons of data is generated every minute and the need for segregation of data for various processes is unavoidable. A minor overlapping of data might cause some unwanted complications and disruptions in the business flow and wrong decision making. Maintaining a separate database for previously funded entrepreneurs and another set for unfunded emerging entrepreneurs can be helpful and is achieved by an AI system effortlessly. Also in the venture capital industry, millions of transactions take place between the VC and the companies. The details of these transactions including financial performance, name of the company, time, and expected return on investment rate are automatically updated in the right database with help of AI. In the past, conventional databases were a common sight in venture capital industries which were mostly paper-based. Searching for a particular transaction in such a database may take hours. But AI-controlled databases provide instantaneous access to required transactions with just a few clicks.
Final Thoughts
Every stakeholder in the venture capital industry is gradually adopting AI to enhance the pace of transactions. But still, there is a long way to go as the adoption of AI in this industry is not as high as in other industries. Adopters are worried that AI implementation may not be successful in such a conventional industry. But, with all the requirements pre-planned and having prior knowledge of the right AI models, AI can be utilized effectively even in this industry. In addition to being a supporting entity to venture capitalists, the AI framework can also aid entrepreneurs to raise funding by continually updating the company’s profile in accordance with the venture capitalist’s interest.
