Everyone understands the nature of a prediction: declaring that something will (or will not) happen, at some point after right now. And how we get there empirically hasn’t changed in principle for centuries, if not millennia: we observe and experience (capture data), analyze our intel (regression analysis), and forecast assumptions based on the conclusions we’ve drawn. This approach — using all available descriptive analytics to derive predictive analytics — has been proportionately consistent over time with the technology available. What is different now, and on an unprecedented scale, is the sheer volume of data generated that can be analyzed, and the rate at which that data is increasing (the oft-cited amount of 2.5 exabytes of daily data created — the equivalent of 250,000 more Libraries of Congress today than yesterday, and another 250k again tomorrow, etc. — is a stat that already dates back a few years, and is therefore becoming more of a historical marker).
Keeping pace with this massive data increase are the tools available via artificial intelligence and machine learning to process that information. Nowhere has this become more apparent than the deployment of AI across the financial services industry, where prediction as a capability is exploding, and those looking to invest wisely would do well to capitalize on this revolution in processing power.
For today’s investor, the question is deciding on just how much of the available tech is desired to meet his or her specific investment needs. The traditional human roles of investment advisor, fund manager, analyst, etc., are still plentiful to assist investors, but more and more that challenge of making information actionable — processing data from hindsight to insight to foresight with respect to successful investing — is being met by the implementation of AI-driven predictive analytics.
It is perhaps useful to consider the scale of the global predictive analytics market and its projected growth in order to better understand its potential when correlated with investment-making decisions. One report claims that this market will reach $6.5 billion by 2019; another forecast has it nearly doubling to $12.41 billion by 2022. And within that subset of big data, the largest percentage of predictive analytics used in future applications will be for the financial services vertical.
And like so many things evolving with tech, access to that vertical is becoming more horizontal each day. An investor — any investor, regardless of purchasing power — now has access to brokerages that either license the use of an AI-driven platform or construct deep-learning frameworks themselves, aggregating vast datasets and constantly analyzing the “4 Vs” of information:
- Volume. The amount of incoming data is voluminous on a scale never seen before.
- Velocity. The speed at which data arrives is beyond human ability to process it.
- Veracity. Machines are necessary to assist in fact-checking so much data.
- Variety. Only machines have the ability to capture multiple data streams, from the historical and fundamental to the quantitative and crowd-sourced.
The continuous stream of data fuels investment-centric predictive analytics, able to execute on the extracted intel in a way that provides the investor with a “fifth V,” Value, and a true edge in generating alpha.
Within all those exabytes of data being created each day exists the equally expanding market of predictive analytics, and this is where AI’s potential for fomenting modern investment strategies can really be harnessed: AI consumes huge amounts of data unsentimentally, and its algorithms are self-learning, able to adapt and advance toward stated investment objectives, all in the service of humans. This is a sea change in active portfolio management — powerful, intelligent machines working in concert with modern, intelligent investors.
TrueRisk Labs builds AI. Our machine learning platforms are constructed specifically for applications across the financial services industry, allowing both investors and fintech professionals access to the power of a true, native artificial intelligence.