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Saving 360,000 hours each year with automated analysis

 

This last week Bloomberg news put out a great look at JP Morgan‘s COIN (Contract Intelligence) service. JP Morgan continues to show large corporation leadership in the FinTech arena. With this latest announcement JP Morgan proves yet again that applying machine learning, machine analysis and computing power does provide an effective edge in the markets. In the case of COIN they are saving 360,000 hours of legal review and reducing error rates during the contract review process.

Sentiment Analysis of news saves 438,000 minutes per year(1)

Applications of computing power and machine analysis such as COIN are the foundation of RelateTheNews. Our innovative sentiment analysis engine transforms over 1000 news articles a day into quantified, actionable insights for financial market participants. This ability to transform such a large volume of textual data in a repeatable manner reduces the chance of missed opportunity hidden in unread news while ensuring substantially greater integration of signals contained within news articles. This time savings is not the sole benefit of using machine analysis on financial market news. The ongoing repeatable quantified analysis also provides signals to be employed in a quantitative investment process to drive alpha.
1 – Assuming a conservative 300 words per article and average reading speed of 250 wpm – RelateTheNews reads the human equivalent of 438,000 minutes per year.