Big Data is Transforming the Financial Industry at its Core

Earlier this year, Investopedia published a very insightful article on the intersection of big data and the financial industry. Big data is changing every facet of the financial industry, from insurance actuary processes to the management of the stock market.

The Progression of Big Data Technology in the Financial Industry

The 21st has been the age of information technology. Information technology and the accumulation of truly staggering amounts of data have transformed the landscapes of countless industries, signaling a paradigm shift in the way we do things.

One of the most acutely affected industries has been the financial industry. This industry thrives on processing market information to figure out what the next big thing is and what to do about it. With the rise of big data, the financial industry has undergone a metamorphosis on a scale rarely seen in nature.

MAPR cited some hard statistics on the role of big data in the financial services industry. They showed that the global big data market is worth $130.1 billion. The demand for big data in the financial services industry accounts for 13.1% of this market “ or $17 billion. Other financial sectors are also utilizing big data and presumably account for around 20% or more of the consumption of big data technologies.

The most prominent financial players out there have taken heed of this trend and are preparing accordingly. From the top investment banks to the best online brokerage, the financial industry is taking full advantage of big data to maximize customer value so that they might remain relevant in the business.

What is Big Data?

Big data is the utilization of the massive data piles we have accumulated to make sense of certain trends and phenomena and to devise a system to make the best of it. Big data has been gaining prominence as we have gotten into the habit of storing every single data point we generate.

Big data is also being made ever more possible with the exponential improvements in the price performance of information technology, storing mountains of data has become cheaper with time, making it even easier to take advantage of big data.

How it Works in the Financial Industry

The financial industry is heavily dependent on the analysis of data to predict what the next market trend is going to be. Based on this analysis, billions of dollars will be deployed into certain asset classes in hopes of making optimal returns.

Because of this, the accuracy of the data analysis is extremely crucial. Any mistake in the analysis could result in the misallocation of billions of dollars into the wrong investment products and vehicles, permanently damaging the reputation of the financial institution responsible for it. Financial institutions have come up with very creative ways of taking advantage of this new technology. 

Algorithmic Trading

The algorithm is given an initial set of instructions and guidelines by which to operate. Things such as risk range tolerance, asset class restrictions, and many more are programmed into the algorithm based on the preferences of the owner.

After the algorithm has been plugged into the financial system, it will be given an initial amount of funds which it will invest based on the guidelines provided as well as the data it manages to retrieve. The algorithm will get its hands on as much historical and relevant information as it possibly can.

With this information, the algorithm will run it through its analytical system, where it will search for patterns and trends that repeat themselves to find ones that can be reasonably utilized in present-day market conditions. Once one is found, the algorithm will apply it to its list of considerations. When a relevant situation shows up, the algorithm will recognize it as such and apply that historical knowledge to the present investment situation. Algorithm trading has gained considerable steam in the past few years and shows no signs of slowing down.

Challenges and Concerns

The main concern amongst some is that of privacy. Although a lot of data that is collected is purely numerical information about the market, there are cases where personal information is also collected, analyzed, and put to use in the real world, for better and for worse.

The major challenge many have cited when it comes to this form of data analysis is that of mistaken correlations. There is a real risk of data analysis teams or even computer algorithms mistaking a correlation between two factors and assuming they must have a causal relationship with them.

Sometimes, certain data lines might be even mistaken for a long-term trend when there might be arbitrary aberrations. Making sure these mistakes are at least kept to a minimum is crucial in maximizing investor returns as well as maintaining the reputation of the financial institution in question.

The Human Touch

It is for this and more that people still require the human touch at some point in the process. Not only is this in line with the Caveman Hypothesis, but it also signifies that nothing on the Earth is perfect, not even a supercomputer. As such, some people still wish to have a human involved during the investment advising process, if only to provide psychological reassurance and to catch any errors that might have been made by any computer program.

The same thing goes for trading machines. Although they can flawlessly search for correlations and other statistical variables, that might not always be enough to execute a good trade. As a result, many financial institutions are still resigning to placing a human being at the end of the process to approve or reject any investment more recommended by these algorithms if they judge them to be mistaken.

The future seems poised to become increasingly digital. As information technology becomes cheaper and better, the number of uses and the sophistication of those uses will continue to increase. As with other phenomena of such transformative potential, there will always be upsides as well as challenges. The trick is to have the wisdom to enjoy the most of what the upsides are and at the same time, face the challenges and even make them into opportunities.