Two advancements in technology over the last few years have resulted in a potential opportunity for investors to begin working in a smarter, more profitable way.
These two technologies are Big Data and Machine Learning (ML). Individually, they are useful technologies; combined, they are greater than the sum of their parts and could change the way we make investment decisions entirely. And research suggests the value propositions of Big Data technologies for the financial services market segment will grow exponentially – a PWC report found that “by 2020 there will be 20 times more usable data than today.”
This short article aims to give an overview of how the two technologies combine into a powerful tool that large financial institutions can use to operate more effectively.
What is Driving the Adoption of ML in Investment Analysis?
The real driver behind the adoption of ML in the investment sector is the mature state of Big Data tech. Just five years ago, Big Data was in its infancy. Although it was apparent that Big Data was going to revolutionize the way we capture, manage and store data, much was still theoretical. Fast forward to today, and Big Data is a mature technology that thousands of companies working in a diverse group of markets leverage. Two factors enabled the Big Data revolution:
Yet this is only half of the equation. All of this Big Data is only useful if we can find ways to extract the insights it contains. This means we need advanced analytics techniques that can interrogate large datasets efficiently. It is where ML comes in. ML has now advanced to such a stage that it can truly begin to extract tangible value from Big Data that investors have been accruing for some years now.
So, to summarize, the maturity of Big Data technology and the growing capabilities of enterprise-grade ML applications have presented investors with something new. A combination of these technologies that could entirely reshape the way they make investment decisions.
How Does ML Help with Making Intelligent Investment Decisions?
The effect that AI will have upon investment will be huge. It is a complete paradigm shift in how large datasets are analyzed and investment opportunities are uncovered. Traditional data analytics will be entirely replaced. Gartner's Vice President Nigel Rayner refers to AI as a "key differentiating factor in finance systems," highlighting the technology's transformative potential in the financial industry segment.
Additionally, as investment houses begin to rely more upon agile, alternative datasets rather than static data such as quarterly reports, investment opportunities will be highlighted much more rapidly. The entire investment market, be it stocks, FOREX, precious metals, etc. will become much more responsive.
Furthermore, there will exist a new market, in the form of dataset collectors that will be able to sell on these unique investment-focused datasets to investment houses.
New Data Analytics Techniques Enabled by ML
The single most disruptive change that Big Data combined with ML will deliver to companies that need to make investment decisions can be found in the source of data that will now be actionable.
Historically, investors use financial data to try and uncover trends. With the Big Data and ML model, we can begin bringing in additional data sources such as consumer spending habits (through payment processor records), consumer attitude (via social networking sites), and advanced data sources such as satellite imagery. Being able to analyze these kinds of alternative data is going to change the investment landscape completely.
The Four Models of AI
In recent years, as multiple tech vendors have begun to work towards delivering narrow focus AI driven tools, four critical models for the integration of AI into the enterprise have been theorized. These can be defined as:
Making a Case for ML as a Driver of Investment Strategy
If we consider the basic overview of how Big Data and ML are going to completely change the way that investments are made, then we can come up with some key takeaways:
These few takeaways combine to tell anyone involved in financial investments that it is time to wake up and become informed. Failure to adopt these technologies will result in a major loss of profit. Put simply, traditional data analytics will not be able to compete at all with Big Data and ML once they become a mature, combined technology.
So, it comes to no surprise that over 70% of the financial organizations operating at a global level are already exploring Big Data and predictive analytics initiatives according to a recent Accenture report.
The original contributed post is here. The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends.