Machine Learning (ML) and Artificial Intelligence (AI) continue to comprise some of the hottest stocks for investors in 2018 — with names like Amazon and NVIDIA capitalizing on the trend thanks to their respective forays into cognitive technology — but they have also remained harbingers for major change within the investing industry as a whole. Today, there are more instances of ML in finance than ever before, with ML tools and computing capabilities increasing in both power and frequency concurrently; this has spawned from cognitive trends surrounding fraud detection, algorithmic trading, loan underwriting, and a variety of other significant industry dimensions.
As this technology has become an integral part of the financial world, it comes as little surprise that more key trends rest on its immediate horizon. That said, here are a few notable considerations in ML-based investing as we near the start of 2019.
Perhaps the most alarming ML implications surround the concept of sentiment analysis, or the technology’s ability to understand and analyze news stories, social media interactions, and other data sources at a human level, transcending the mere absorption of stock prices and trades alone. As observed by TechEmergence, “the stock market moves in response to a myriad human-related factors that have nothing to do with ticker symbols (like those commonly showcased in the aforementioned news stories and data sources),” and ML algorithms are being designed to replicate such “human intuition” at an enhanced level by reading and accurately interpreting potential trends.
ML algorithms are becoming capable of recommending financial services and products — based on any number of individualized metrics — and this process only continues to become more personalized as the technology evolves. In the coming year, expect these types of automated services to be perceived as “more trustworthy, objective, and reliable –” maybe even more so than human advisors. This notion is exciting, especially when considering its impact on portfolio advisement, insurance recommendation, and overall financial planning (among other areas); quick and convenient automation of these services are already changing the financial landscape, and increased efficiency will serve as a natural complement to current activity.
It is no secret that ML has led to increased security in both investing and a wide variety of other industries. Cognitive technologies are already able to accurately detect fraud despite a growing “perfect storm” of data-based security risks, detecting unusual activities and flagging them as potentially dangerous anomalies. The key now is to make sure these services are as accurate and safe as possible, as the call for security enhancement has only grown louder in the age of investing apps rooted in e-commerce and online banking.
Looking ahead, investing-based ML security will strive to sharpen itself by mitigating flagged “false positives,” or nonthreatening activity that is flagged as a risk, while also challenging longstanding paradigms in cybersecurity’s storied history; this means the potential elimination or reimagining of passwords, usernames, and similar protective forces to keep users as secure as possible.