How Big Data Has Changed Finance

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How Big Data Has Changed Finance

What Is Big Data?

The massive explosion of data, along with increased technical sophistication, is transforming the way companies function and compete. Over the last several years, 90 percent of the world’s data has been generated as a consequence of the daily generation of 2.5 quintillion bytes of data. This fast accumulation and storage of structured and unstructured data, colloquially known as big data, presents possibilities for data gathering, processing, and analysis.

How Big Data Works

Organizations utilize data and analytics to get important information to guide better business choices, following the four V’s of big data. Financial services, technology, marketing, and health care are just a handful of the industries that have embraced big data. Big data use continues to reshape the competitive landscape of sectors. According to an estimated 84 percent of firms, those without an analytics strategy risk losing a competitive advantage in the market.

Big data analytics has been increasingly embraced in financial sectors, in particular, to guide better investment choices with consistent returns. Algorithmic trading, in combination with big data, combines large amounts of historical data with complicated mathematical models to optimize portfolio returns. The ongoing deployment of big data will undoubtedly change the financial services sector. However, despite its obvious advantages, substantial obstacles remain in big data’s capacity to collect the growing amount of data.

4 V’s of Big Data

The four Vs of big data are volume, variety, veracity, and velocity. Financial institutions are looking for new methods to harness technology to achieve efficiency as they face increased competition, regulatory limits, and client demands. Companies may exploit various parts of big data to achieve a competitive edge, depending on the industry.

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The rate at which data must be stored and processed is defined as velocity. Every day, the New York Stock Exchange collects 1 terabyte of data. There were an estimated 18.9 billion network connections in 2016, with around 2.5 connections per person on the planet. Financial institutions may distinguish themselves from the competition by concentrating on processing deals effectively and rapidly.

Big data may be classified as either unstructured or structured data. Unstructured data is information that is not organized and does not fit into a certain paradigm. This includes information gleaned from social media sources, which assists institutions in gathering information about client demands. Structured data is information that the company already manages in relational databases and spreadsheets. As a consequence, in order to inform better business choices, numerous types of data must be actively handled.

The growing amount of market data is a significant problem for financial organizations. Banking and financial markets must actively maintain ticker data in addition to huge historical data. Similarly, investment banks and asset management organizations rely on large amounts of data to make effective investment judgments. For active risk management, insurance and retirement organizations may examine prior policy and claim information.

Algorithmic Trading

Because of the increasing powers of computers, algorithmic trading has become associated with big data. Computer systems can now perform financial deals at speeds and frequencies that a human trader cannot. Algorithmic trading inside mathematical models allows transactions conducted at the best possible pricing and fast trade placement while reducing human mistakes due to behavioral variables.

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Institutions may more effectively limit algorithms’ ability to integrate enormous quantities of data by exploiting large volumes of previous data to backtest methods, resulting in less hazardous investments. This assists users in identifying beneficial data to preserve and low-value data to delete. Given that algorithms can be built with both structured and unstructured data, combining real-time news, social media, and stock data in a single algorithmic engine may result in improved trading judgments. In contrast to decision making, which may be impacted by a variety of sources of information, human emotion, and prejudice, algorithmic trades are based exclusively on financial models and statistics.

On a digital platform, robo advisers employ investing algorithms and enormous volumes of data. Modern Portfolio Theory is used to structure investments, which often favors long-term investments to sustain consistent returns and involves little involvement with human financial advisers.


Despite the financial services industry’s growing acceptance of big data, substantial difficulties remain. Most notably, the acquisition of varied unstructured data supports privacy concerns. Personal information regarding an individual’s decision-making may be acquired via social media, emails, and health data.

The bulk of criticism in financial services is directed at data analysis. The sheer amount of data necessitates more statistical complexity in order to generate correct findings. Critics, in particular, overestimate signal to noise as false correlation patterns suggesting statistically robust conclusions solely by coincidence. Similarly, algorithms based on economic theory generally lead to long-term investment possibilities based on past data patterns. Predictive models have inherent obstacles in generating data that support a short-term investing plan.

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The Bottom Line

Big data is continuing to change the landscape of several businesses, most notably financial services. To preserve a competitive advantage, several financial institutions are using big data analytics. Complex algorithms can perform trades utilizing a variety of data sources using organized and unstructured data. Human emotion and prejudice may be reduced with automation; yet, trading with big data analysis has its own set of obstacles. Because of the field’s relative freshness, the statistical findings published so far have not been universally adopted. However, as financial services move toward big data and automation, statistical approaches will get more sophisticated, increasing accuracy.

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