How Big Data Is Changing Banking And Finance

by Sohini Bagchi    Jan 16, 2018

banks

Today, most banking, financial services, and insurance (BFSI) organizations are striving to adopt a fully data-driven approach to grow their businesses and enhance customer services. Experts believe, like most other industries, big data analytics will be a game changer for companies in the financial sector. However, despite and growing interest and the increased rate of adoption of analytics to gather immense volumes of data assets, BFSI companies are at varying levels of Big Data maturity. One of the key challenge remains is that how can this data help them solve critical business problems?

From data to insights

A Frost & Sullivan whitepaper reveals that analytic insights hold the key to solving pressing issues and capturing market opportunities. According to the whitepaper, Bitcoin and other new technologies are beginning to provide consumers with alternatives to potentially replace banks altogether. However, disruptive market forces are far from the only challenges financial institutions face.

While many are beginning to do a respectable job of managing big data, the pace of business now demands that they do it faster than ever before— while meeting the ongoing challenge of reducing costs without sacrificing customer experience. Financial institutions must not only help teams across the enterprise understand their data but also measure the impacts of their actions and adequately communicate insights.

When used effectively, banks can use the vast array of customer information to continually track client behavior in real time, providing the exact type of resources needed at any given moment. This real-time evaluation will in turn boost overall performance and profitability, thus thrusting the organization further into the growth cycle.

Improving customer experience

Customer-centric objectives play a key role among enterprises that deal with data–related activities and the BFSI sector is no exception. With the advent of mobile banking and social media banking, today’s customers have high expectations on the ways of how they interact with their banks or credit unions. Their buying journey is complex and non-linear so financial players must be able to carefully understand customer preferences and motivation.

Data-fueled analytics can empower those in the BFSI sector with customer insights and help create customer segmentation. According to a new data from McKinsey reveals that using data to make better marketing decisions can increase marketing productivity by 15-20% – that’s as much as $200 billion given the average annual global marketing spend of $1 trillion per year.

Optimizing business operations

Big Data technology can improve the predictive power of risk models, improve system response times and effectiveness, provide more extensive risk coverage. In the process, it can generate significant cost savings by providing more automated processes, more precise predictive systems, and less risk of failure. However, in the BFSI sector, many companies are yet to unlock its risk management power.

Experts believe, there are many areas in risk management where Big Data can apply and bring value, including fraud management, credit management, market and commercial loans, operational risks, and integrated risk management.

Greater employee engagement

When done right, big data analytics can help track, analyze, and share employee performance metrics. Applying Big Data analytics to your employees’ performance helps you identify and acknowledge not only the top performers, but the struggling or unhappy workers, as well. These tools allow companies to look at real-time data, rather than just annual reviews based on human memory.

Big data tools can measure everything including individual performance, collaboration between departments, and the overall company culture. When the data is related to customer metrics, it can also enable employees to spend less time on manual processes and more time on higher-level tasks.

banks

Overcoming challenges

Despite the financial services industry increasing embrace of big data, significant challenges still exist in the field. Most importantly, the collection of various unstructured data supports concerns over privacy. Personal information can be gathered about an individual’s decision making through social media, emails and health records.

Within financial services specifically, the majority of criticism falls onto data analysis. The sheer volume of data requires greater sophistication of statistical techniques in order to obtain accurate results. In particular, critics overrate signal to noise as patterns of spurious correlations, representing statistically robust results purely by chance. Likewise, algorithms based off of economic theory typically point to long-term investments opportunities due to trends in historical data. Efficiently producing results supporting a short-term investment strategy are inherent challenges in predictive models.

“Despite the challenges posed by various external and internal forces, there is good news here, and plenty of it, in the form of big data. If managed effectively, big data can provide the actionable analytic insights that hold the keys to solving these pressing issues, and to new areas of opportunity,” said Jeff Cotrupe, industry director, Big Data and Analytics, Stratecast | Frost & Sullivan. “A growing number of financial institutions are seeing the light: big data expenditures in this sector accounted for 19 percent, or $9.2 billion, of the $48.4 billion global BDA market in 2016, addressing issues and opportunities including security and privacy; data governance and blockchain; risk management and regulatory compliance; AI and IoT.”

One reason for the spending, said Cotrupe is that financial institutions are achieving an excellent return on their BDA investment. Nearly 60 percent of them achieve ROI in 12 months, and nearly 90 percent have attained ROI by the 24-month mark. 

Many financial institutions are adopting big data analytics in order to maintain a competitive edge. Through structure and unstructured data, complex algorithms can execute trades using a number of data sources. Though the implementation of Big Data on a large scale has just started to evolve in the BFSI industry, financial services trend towards big data and automation, as well asd sophistication of statistical techniques will lead to greater accuracy and cost benefits.