How Big Data is Transforming The Landscape of The Financial Industry
The days of the picturesque accountant hovering over an adding machine are long gone. And, by the looks of it, the duties of the modern day accountant may soon be taken over as well. With advanced technology comes more conventional methods. So what is the technology that is finding its way into nearly every financial services sector and transforming the whole industry?
Big data is effectively consuming some current financial duties while defining others — reinventing the classical role of an accountant into a more modernized player, relying on analytics to more accurately perform financial operations. Almost all financial duties involve data in one way or another, so it’s only logical to implement big data sciences into the mass amount of financial data a company produces daily.
Read below to find out the ways that big data is playing a pivotal role in transforming the financial services landscape.
Trading And Investing
The hustle and bustle of wall street buyers and sellers is nothing compared to what machine learning can do. Machine learning can look far beyond just analyzing the buying and selling prices to determine the best stock options for you or a company. It can take into account social and political trends and can even consider trending companies and buzzwords on social media to make very current decisions on said stock options. Furthermore, machine learning can make these decisions in real-time — something that is impossible for a human. Humans will, however, have to step in to compile and evaluate this data. In this way, big data is creating less of a role for a typical investor, but more of a data analyst position.
In any instance where massive data needs to be collected and recollected with precise accuracy, big data analytics can prove to be extremely useful and applicable — and taxation is one of these instances. Taxes are done every year, whether for business or personal reasons, and there is a large volume of financial data from past years to the present. Software today can easily access the data from previous years as well as the current year’s filings, eliminating human error from filing your taxes. Take advantage of international tax advisory services and ability to provide an extensive range of services to help multinational organizations meet their inbound and outbound tax, help U.S. businesses open foreign operations, and assist foreign businesses.
Although it will eliminate human error, big data won’t remove humans from the tax process altogether. Tax experts at Villanova University shed some light on the new role a tax professional will play in the upcoming years of big data analytics and taxes, stating that, “experienced accountants who have earned a Master of Taxation are likewise needed to shepherd the data that is collected, managed, and organized, to ensure that the analytic potential of modern technology is leveraged responsibly and accurately.”
Fraud Detection And Investigation
Almost every financial transaction leaves behind a trail of data. As a result, this data can be assessed and monitored through machine learning to understand your buying habits — what stores you shop at, how much you usually spend in a month, etc. If your credit card information is stolen, or machine learning analytics detect any transactions contradictory to your buying habits, it can alert your bank instantly so they can put a hold on the transaction until they can figure out if the purchase is fraudulent or not.
Since credit cards produce so much data and can quickly fall into the wrong hands, fraud has become rampant. Machine learning is helping to police this illegal activity, stopping fraud before it even starts. In many cases, you’ll receive a notification on your smartphone asking if you are indeed the one making the purchase. If not, then you can halt the transaction and start the process of regaining your financial privacy and security.
Big data’s predictive analytics can run risk management far more accurately and faster than a human. Taking into consideration the many aspects of a sound financial decision can be time consuming for humans, but can take seconds for a machine. Take a simple bank loan for example. Machine learning can take into account the current economy, your business capital, customer segmentation, and many more factors instantly to come to a proper decision.
Financial decisions based on human instinct, such as investments and loans, can land a company in hot water if decided incorrectly. In the finance world, doing “what feels right” can sometimes turn out not to be in your best interest. Machine learning is an unbiased, calculated way to make the decisions that will be better for you and your company.
Big data and machine learning are prevalent in many industries because they can automate many of the mundane duties that employees are tasked with every day. This automation leaves more time for employees to focus on more significant and complex issues. The financial services industry produces an ample amount of data points for big data analytics to automate the tasks above and many more.
This automation is what is transforming the role of the modern accountant. Arizona State University understands the transformational power big data is having in the financial services realm and provides insight on what a financial analyst does, stating that, “These professionals track the analytics behind stocks, bonds and other investments in order to counsel clients on building and maintaining their portfolios.” With the automation of spreadsheets, taxes, and other financial operations, new professional roles — such as a financial analyst — will have to work with this data to make judgments about the economic future of a company.
Big data and data sciences are not bulletproof and will need human supervision for governance. However, it is continually transforming the financial services industry in such a way that it is rendering some roles obsolete while creating new positions. Big data is not only doing this to the financial services industry, but every industry that could benefit from the management, storage, and easy accessibility of massive amounts of data — which is virtually any industry.
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