Post sponsored by NewzEngine.com

Source: MIL-OSI Submissions
Source: SourceScrub

You’d be surprised how much artificial intelligence (AI) and machine learning (ML) is involved in industries like investing, banking and mergers and acquisitions. These programs silently work in the background and over the past few years, have made tremendous advancements in how investors are able to find the best companies to invest in.

SourceScrub is making a major impact in the industry by taking thousands of sources and providing an organized and easy way to access information in a platform purpose-built for finding, researching and connecting with privately held companies. Founded by Tyler Fair and Prescott Nasser, Sourcescrub is helping dealmakers connect with companies that are ready for investment, but probably not on anyone’s radar yet.

SourceScrub CEO Tyler Fair says, “Dealmakers are looking for the one-in-a-million company that will have the highest growth potential while still meeting their funds investment strategy. With millions of private companies to consider, this is a massive task. Machine Learning and Artificial Intelligence are now being used to scale and accelerate these processes that have historically been managed with manual effort.”

The challenge for dealmakers is that datasets used to train learning models typically include inaccuracies that prevent ML-based solutions from living up to potential. SourceScrub solves this problem with a human-supervised learning approach. Models are trained, monitored for accuracy with human curation, and retrained with the latest available data on an ongoing basis.

Fair goes on to say that, “Machine Learning and AI that is derived from a highly curated private company dataset provides a supervised learning approach to enrichment of data. Models are trained, monitored for accuracy with human curation, and retrained with the latest available data on an ongoing basis. This allows ML and AI models to best represent the accuracy and care provided by a dedicated research team. When used in isolation without supervision and ongoing monitoring and retraining, it can lead to bias and incorrect data.”

MIL OSI