Big data in finance refers to large, diverse (structured and unstructured) and complex sets of data that can be used to provide solutions to long-standing business challenges for financial services and banking companies around the world. The term is no longer just confined to the realm of technology but is now considered a business imperative. It is increasingly leveraged by financial services firms to transform their processes, their organizations, and the entire industry.
– Big data in finance refers to large, diverse (structured and unstructured) and complex data sets that can be used to provide solutions to long-standing business challenges.
Big data is completely revolutionizing how stock markets across the world are functioning and how investors are making their investment decisions.
However, the inability to connect data across organizational and department silos is becoming a major business intelligence challenge, particularly in banks where mergers and acquisitions create countless and costly silos of data.
The exponential growth of technology and increasing data generation are fundamentally transforming the way industries and individual businesses are operating. The financial services sector, by nature, is considered one of the most data-intensive sectors, representing a unique opportunity to process, analyze, and leverage the data in useful ways.
Traditionally number crunching was done by humans, and decisions were made based on inferences drawn from calculated risks and trends. However, in recent times, such functionality is usurped by computers. As a result, the market for big data technology in finance offers inordinate potential and is one of the most promising.
Big data is completely revolutionizing how the stock markets worldwide are functioning and how investors are making their investment decisions. Machine learning – the practice of using computer algorithms to find patterns in massive amounts of data – is enabling computers to make accurate predictions and human-like decisions when fed data, executing trades at rapid speeds and frequencies.
Big data analytics presents an exciting opportunity to improve predictive modeling to better estimate the rates of return and outcomes on investments. Access to big data and improved algorithmic understanding results in more precise predictions and the ability to mitigate the inherent risks of financial trading effectively.
Today, customers are at the heart of the business around which data insights, operations, technology, and systems revolve. Thus, big data initiatives underway by banking and financial markets companies focus on customer analytics to provide better service to customers.
Companies are trying to understand customer needs and preferences to anticipate future behaviors, generate sales leads, take advantage of new channels and technologies, enhance their products, and improve customer satisfaction.
Financial organizations use big data to mitigate operational risk and combat fraud while significantly alleviating information asymmetry problems and achieving regulatory and compliance objectives.
Banks can access real-time data, which can be potentially helpful in identifying fraudulent activities. For example, if two transactions are made through the same credit card within a short time gap in different cities, the bank can immediately notify the cardholder of security threats and even block such transactions.