Big Data pushes for optimized business models in financial industry

  • On September 12, 2018

Data drives the modern financial industry in many ways. The financial services sector generates a large volume of data collected from customers, transactions, global trading, and many other sources. It is currently one of the most risk-laden sectors.

On the one hand, this has put the sector under increased scrutiny from regulatory bodies to remain compliant and there is on-going pressure for effective information governance. On the other hand, this also provides a huge opportunity to improve competitiveness and drive business growth. The sector has continued to use data to detect and manage the rise in fraud and financial crime, develop competitive pricing, manage risk and compliance as well as make strategic business decisions.

Alongside helping with regulatory compliance and fraud prevention, the financial industry is leveraging big data analytics to gain insights on how customer behavior evolves and how patterns of client usage may influence retail banking offerings and digital marketing.

A growing number of financial institutions (banks, insurers, asset managers and investor firms) are expected to start using smarter data in their ways of doing business. International Data Corporation (IDC) expects the big data & analytics, mobility and cloud to take up almost 30% of the financial industry’s IT budgets globally by 2019.

“By 2020 there will be 20 times more usable data than today.”

International Data Corporation

According to a recent IBM survey, 71% of banking and financial markets firms report that the use of information (including big data) and analytics is creating a competitive advantage for their organizations. These organizations are extracting new insights from existing and newly available internal sources of information, defining a big data technology strategy and then incrementally extending the sources of data and infrastructures over time.

NYSE Euronext Big Data adoption – business case example

NYSE Euronext for example, employed big data analytics to detect new patterns of illegal trading. The stock exchange reports that the new infrastructure has reduced the time required to run markets surveillance algorithms by more than 99%. The volume of IT resources required to support the solution decreased by more than 35%. At the same time, the ability of compliance personnel to detect suspicious trading patterns has improved. This way NYSE Euronext managed to reduce damage to the investing public.

Research suggests the value propositions of big data technologies for the financial services market segment will grow exponentially. According to IDC report, “by 2020 there will be 20 times more usable data than today.”

Information management is getting harder every year. Solving big data challenges requires the management of large volumes of highly distributed data stores along with the use of compute- and data-intensive applications. Virtualization provides an additional level of efficiency to make big data platforms a reality. Although virtualization is technically not a requirement for big data analysis, software frameworks are more efficient in a virtualized environment.

Financial institutions can gain substantial benefits from data virtualization in many areas, including cost reduction, agility in implementing projects where data integration is key, increased control over data and compliance despite increasingly strict regulations, increased data quality and consistency, and the easy implementation of 360-degree client views. Data virtualization plays a role in many other data management areas involving in-depth needs analysis and issue resolution.

One thing is certain, though: big data analytics is not a one-hit wonder. It is surely here to stay and alter the way global financial institutions do business.

Sources:

https://www.idc.com, International Data Corporation, Worldwide Semiannual Internet of Things Spending Guide (version 2H17)

https://www.ibm.com/ IBM Institute for Business Data, Analytics: The real-world use of big data