New research in which machine-learning models were trained to verify more than one million companies has demonstrated that artificial intelligence (AI) can precisely quantify the success and failure aspects of a startup. The outcome is a tool that allows investors to identify the next opportunities. A known fact is that about 90% of startups are unsuccessful - about 10% to 20% fail within their first year. This shows the notable risk to Venture Capitalists and other investors in early-stage companies. In an attempt to identify which companies are most likely to succeed, researchers have developed a machine-learning model trained on the historical performance of more than one million companies.

A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool, Venhound, that has the potential to help investors identify the next unicorn. It is well known that around 90% of startups are unsuccessful: Between 10% and 22% fail within their first year, and this presents a significant risk to venture capitalists and other investors in early-stage companies. In a bid to identify which companies are more likely to succeed, researchers have developed machine-learning models trained on the historical performance of over 1 million companies. Their results, published in KeAi's The Journal of Finance and Data Science, show that these models can predict the outcome of a company with up to 90% accuracy.

A study in which machine-learning models were trained to assess over 1 million companies has shown that artificial intelligence (AI) can accurately determine whether a startup firm will fail or become successful. The outcome is a tool, Venhound, that has the potential to help investors identify the next unicorn. It is well known that around 90% of startups are unsuccessful: Between 10% and 22% fail within their first year, and this presents a significant risk to venture capitalists and other investors in early-stage companies. In a bid to identify which companies are more likely to succeed, researchers have developed machine-learning models trained on the historical performance of over 1 million companies. Their results, published in KeAi's The Journal of Finance and Data Science, show that these models can predict the outcome of a company with up to 90% accuracy.