Given the ever-growing volume of applicable data, deep learning architectures will become essential for the future of investment research. These models have been practically proven to be the most accurate learning models when handling large terabyte or petabyte scale datasets.

With increased computational power and sample size, deep learning architectures do more than identify general, statistical trends among typical gigabyte scale datasets. They allow us to go one step further, identifying even more subtle, statistical relationships and decisions with the promise of generality and reduced overfitting. The widespread availability to run deep learning architectures on consumer cloud platforms like AWS has really only been prevalent for the past 4-5 years, not so coincidentally timed with the incorporation of Trill A.I.

Today it is no longer enough to just talk about artificial intelligence. The hype of AI has passed. It is time to roll up our sleeves, granularly explore the various AI techniques working behind the scenes and determine the elements and solutions that are right for your organization.

Think about the data that is used to analyze, recommend and allocate clients’ investment portfolios. Now think about all the data that is disregarded as less relevant due to constraints on time and budget. That data could be productively analyzed in real-time. Incorporating that data is where deep learning architectures begin to shine.

Deep learning saves time and money compared to classical machine learning

Classical machine learning models are inherently people-driven and require an intimate understanding of all available datasets. These types of quantitative techniques are effective and reflect human understanding, but they come with their own limitations. They are more susceptible to bias and overfitting because they plateau in performance after consuming much less data, and the only way to improve a classical machine learning model once it has plateaued is to change or rebuild the model’s features and architecture manually.

Deep learning techniques work differently than classical machine learning models. Instead of having to filter, select and engineer features, deep learning models are able to work from un-engineered features and even raw data and have been shown to have virtually no plateau when introducing more data into a pre-existing architecture.

That single differentiator is significant. Deep learning models create an environment where improvement and decision making is not bottlenecked by the time constraints or creativity of human researchers. If you have too much data and too little time, deep learning is for you.

Deep learning will soon become necessary, rather than a nice-to-have

With smarter, more connected technologies continuously entering the market, the volume of data to analyze for investing purposes is growing at a rapid pace. Soon it will not be possible for human experts to analyze all necessary data sources independently. That is where deep learning comes into play, with powerful algorithms that will become a critical component to the investment process. These systems are being developed so they will improve from real-time data, and the more automated and scalable these processes become in the future, the more necessary they will be.

Deep learning is here to stay and companies are adapting

Not understanding how deep learning works or how it can be integrated into your operations is no longer excusable. Today’s investment process requires methods to handle the expanse of data becoming available and relevant. Ignoring them and using outdated techniques under the guise of “interpretability” will also no longer be an option. Deep learning output is rapidly becoming equally interpretable to any other algorithmic process; it is an avid area of research. In fact, explanations are already available for all Trill A.I. deep learning outputs.

Clients are gravitating to the firms that are providing the best care for their hard-earned dollars. Will yours be one of them?

Image: Unsplash