<img height="1" width="1" style="display:none;" alt="" src="https://dc.ads.linkedin.com/collect/?pid=586106&amp;fmt=gif">

SUBSCRIBE HERE


Posted by Nick Potts
Nick Potts

Not long ago, if hedge funds wanted to gauge the sales performance of a retailer like JC Penney, they would send Junior Analysts out to the stores to count cars in parking lots. This ‘channel check’ approach – and the limited insights it produced – eventually gave way to ‘Alternative Data.’ An example being satellite imagery which offers the same information (number of cars in parking lots), only in much larger, more dependable data sets. When it used to take an army of human analysts weeks or even months to accomplish, a satellite can perform in mere days.

Yet due to the exponential growth rate of digital information, investors are now bombarded with an overwhelming number of data sets. In our JC Penney example, investors have more than just satellite imagery to sift through; there’s data on customer sentiment culled from social media posts and product review websites, and on company sentiment gleaned from the language and nuance used in earnings calls. The burden of transforming all of that raw data into actionable intelligence is far too great for even the savviest of fund managers, especially when human bias impacts decision-making (as it inevitably does).

So how to proceed?

In order to connect the dots, investors are increasingly relying on cognitive systems – not to supplant human decision-making, but to support it. AI can aggregate and classify all of that raw data and derive optimal solutions, which can then be used by humans to predict future outcomes.

The key is partnership; it is the machines that make sense of the data, and humans who interpret the findings. If humans do all of the legwork (as was the case when Junior Analysts were sent into parking lots), the result is inefficiency and small, trivial data sets. While machines can enlarge and optimize those data sets, they are very bad at accurately predicting outcomes – for that, human cognition is still king.

So we’re talking about a marriage of convenience here. To that end, Accrete has built a framework based on the iterative collaboration between domain-specific expert human networks and artificial intelligence.

 

Accrete-Framework

This powers Accrete's investment tools and is designed to help fund managers generate alpha by applying cognitive technology to real-world problem solving. Our platform enables investors to extract actionable insights from subtleties and nuances hidden in text and language, with greater accuracy and efficiency than either human or machine-driven efforts can independently offer.

The goal of active management has always been to identify and act on investable opportunities before they are uncovered by the broader market. The connections and insights afforded by unstructured data analysis tools allow investors to do just that. No longer do funds have to send their juniors out to count cars in parking lots. Accrete AI combines the best of both human and machine capabilities, and delivers the informational advantage investors have long been searching for.

View the interview by NASDAQ with Prashant Bhuyan, CEO of Accrete.AI who details the vision of the future of artificial intelligence and how it can augment investors abilities to make smarter decisions.

Nasdaq Interview with Accrete


Topics: Artificial Intelligence