As reported by Statista, the world’s Big Data and business analytics market was estimated at ＄168.8 billion in 2018 and is determined to arise to ＄274.3 billion by 2022. Fair enough, since the finance section leaders are already leveraging Fintech Software Development Services to bring their processes to a new level.
Undoubtedly, Big Data has closely integrated with the financial services industry, and it’s very likely to keep on driving future innovation. Stay with us to learn what is the role of Big Data in finance, and which significant challenges it might present.
Being a real buzzword these days, the term ‘Big Data’ entails large volumes of data that carry on increasing exponentially, as well as approaches to its collection, processing and analytics. According to IBM data scientists, Big Data is based upon ‘4V’s’ volume, variety, velocity and veracity.
Work means the scale of data; combination implies its diverse forms, the rate stands for the acceleration at which data is the store and analyzed, and finally comes integrity, which might be questionable.
It comes without saying that the financial services industry is one of the most data-intensive sectors of the global economy, so the role of Big Data in this area is enormous.
Big Data gives a competitive advantage to companies, enabling them to work faster and more efficiently. In this section, we’ll walk you through the most common application of Big Data in the financial sector.
Let’s be honest: the consumer has always been the critical point of any business. Therefore, banks and financial markets leverage Big Data to collect information that helps identify trends and get insights for predicting customer behaviour and providing better service.
By understanding customer needs, companies get clues on how they can generate more leads, improve their products and services, and increase customer satisfaction. BankMobile, a digital U.S. bank, which partners with universities to get new clients, uses Big Data to easily monitor the effectiveness of these partnerships, its customer acquisition costs, fraud, and also process whopping amounts of customer feedback.
Big Data has changed how stock markets across the globe used to operate, as well as the approach to making investment decisions.
Machine learning provides accurate forecasts at lightning speed, enabling analysts to make the best decisions. In essence, paired with algorithmic trading, Big Data looks incredibly promising for the trading sector.
Big Data analytics is here to enhance predictive modelling for more efficient assessment of the outcomes on investments. Big Data, coupled with improved algorithmic understanding, gives way to more accurate predictions and the ability to alleviate issues related to trading.
As finance industry players operate in high-risk environments, they are in constant need of risk management solutions.
Big Data is a fundamental mechanism in risk management as it copes with evaluating data from multiple sources. This approach allows for tracking, detecting, and alleviating risks that might exert adverse effects on the operations.
With predictive analysis, Big Data allows for detecting fraudulent activities, and many pioneering organizations have already adopted this approach.
For instance, Alibaba Group developed a fraud risk management system that is based upon real-time Big Data processing. The system analyses large volumes of consumer data in real-time and detects fraudulent transactions.
The most significant challenge related to Big Data is its enormous volumes that might be difficult to store and analyze. Also, a great deal of data is unstructured, like documents, audio or photos, e.i.
It doesn’t reside in a database and can be challenging to search and process. Therefore, banks have to invest in the Big Data technologies to support the new use cases, for instance, in NoSQL.
It includes an array of different database technologies that provide a mechanism for accumulation and retrieval of data. A NoSQL database can store unstructured data and give a faster performance, offering flexibility while handling multiple types of data at a massive scale.
Next, data typically exists in separate systems, which makes it difficult to centralize. To extract data from those systems, Big Data requires a near real-time delivery, presenting a challenge for Fintech.
Security is another concern related to Big Data. Because of data centralization out of the protected systems, data security might become questionable. Plus, Big Data stores can become sensitive to hacker attacks. All in all, the financial industry needs reliable solutions to ensure maximum data security.
Also, Fintech has to face the challenge of data integration as the data you need to interpret usually originates from various sources and in multiple formats. As an example, an eCommerce company needs to analyze data from website logs, social media and call-centres. These data formats differ, and it can be challenging to integrate them.
Financial institutions have to keep an eye on the legal restrictions that apply to data collection and processing. For instance, the European Global Data Protection Regulation requires that organizations can store personal data only when there is a direct use for it, the customer being in full control of the data.
In essence, Big Data is genuinely revolutionizing the financial services industry, allowing for improved analytics, anticipating risks and fighting fraud. Still, some challenges need to be tackled to expand efficiency and provide better solutions.
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