Big Data in Banking: Many Challenges, More Opportunities

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8 min read

As the importance of big data grows, its effects on the banking industry are becoming more and more apparent. Online-only banks are becoming the norm, banking executives report mounting concern about technological changes and legacy systems are struggling to keep up.

However, the future holds a great deal of potential for positive change, and a few of the most forward-thinking organizations are already capitalizing on that. In my last post, I talked about how financial services organizations are winning with data. Now, let's find out what's going on in the world of banking today, and what's likely to happen tomorrow.

The Banking Landscape Today

Before we delve in to the impact of big data, let's learn a bit more about the role of modern technology in the banking sector.

Both commercial and retail banks are contending with ongoing risk, compliance and lack of perspective into their breadth of products and clients. Specialist or niche providers, such as Silicon Valley Bank, continue to win market share across both consumer and commercial banking services such as lending, treasury and card.

Meanwhile, traditional retail banks with physical branches and high headcounts continue to offer valuable services to consumer and business clients. However, they are struggling to compete with online-only banks, which offer many of the same services as their fixed-location competitors, but with lower fees and higher savings rates.

For this reason, online-only banks have successfully broadened their customer appeal, especially among newer generations: a survey conducted in 2016 by Bank of America found that Millennials (68 percent) and Gen Xers (70 percent) are the age groups most likely to use digital banking services, compared to the 62 percent average across all age groups.


The online-only banks, which are part of the FinTech movement, are also increasingly adept at offering self-service features, particularly through mobile applications. Once again, this serves to broaden their appeal with newer generations.

This shift hasn't gone unnoticed: in a survey conducted in 2016 by PwC, 25 percent of respondents reported a concern that their business could be lost to standalone FinTech companies within five years.

And, 81 percent banking CEOs-more than any other industry-are concerned about the speed of technological change.

On the lending front, peer-to-peer lending marketplaces are growing faster than traditional bank lending, while traditional banks fight back by creating small loan networks.

In this space, top imperatives include advancing omni-channel marketing capabilities and developing impactful user converged applications while managing risk and compliance.

How FinTech Factors

FinTech is a major disruptive force in the financial services industry.

By definition, FinTech refers to the fast-moving, predominantly mobile and online-only financial services companies or solutions. In its 2017 Global FinTech Report, PwC found that on a global scale, 88 percent of incumbents believe part of their business is at risk due to the rise in FinTech, a 5 percent increase from last year.

Global FinTech Report

Knowing this, it's not surprising that 82 percent of all companies expect to increase their partnerships with FinTech companies over the next three to five years. This percentage can vary from country to country, though in all countries it remains well over 50 percent.

Big Data Challenges

As big data gets bigger by the day, a wider variety of data is being generated by an increasing number of sources. This new data is both structured and unstructured, and legacy data systems are simply unable to handle the volume, variety and velocity of data flowing in.

With so many different types of data, it's no surprise that the sheer volume of data is one of the biggest challenges facing the banking industry.

To illustrate this, consider that the digital universe is expected to reach 44 zettabytes (that's 44 trillion gigabytes) by 2020.

For banks, the challenge makes itself apparent when trying to sort through data that is useful and data that isn't. To complicate things further, the percentage of potentially useful data is growing. This means that more data will have to be analyzed, and less data can be justifiably thrown out.

Data that is Useful if Tagged & Analyzed

This problem is already making itself known: legacy systems are already at their breaking point and struggle to capture, store and analyze unstructured data without the help of added IT complexity.

The Opportunities

To meet the challenges facing them, financial services need a new unified data platform that converges all data into a data fabric that allows them to store, manage, apply and analyze data with speed, scale, and reliability.

Banks know this, and in response they're putting an increasingly greater emphasis on converged platforms capable of running intelligent applications. By learning patterns and deriving insights independently, these intelligent applications vastly improve the time-to-market of new products, and also support modeling and analysis of programs and products currently in the market.

And, because fewer and fewer banks are gleaning information about their customers from face-to-face interactions, these new platforms are helping banks gain a 360-degree view of their customers by integrating sentiment data with customer data and broader market trend data.

New data management platforms are capable of not only capturing and storing enormous volumes of structured and unstructured data, but also creating a global data fabric that enables simultaneous analytics and applications. This allows financial services banks to integrate new applications into their operations in real time.

As banks with more mature data science groups push analytical boundaries, machine learning and the predictive analytics will continue to receive a great deal of attention.

There's a focus on automated applications, where big data drives what the application does without the need for human intervention. This is reflected by a notable uptick in AI-related funding: According to McKinsey, external investment growth in AI has tripled since 2013.

External investment growth in AI

And, unsurprisingly, the financial services sector is one of the strongest adopters of AI.

Financial Sector

In the coming year, the focus will be on cybersecurity and other security initiatives as well as customer activity. With new data platforms, the power of data and machine learning can be harnessed and leveraged to illuminate new pathways in business-critical areas of operations, including client and market optimization, holistic risk management and regulation and compliance.

The Future is Bright

In truth, we've only just scratched the surface of the multitude of challenges faced by banks when it comes to big data. Despite this, we're confident that companies who are willing to meet those challenges head-on will find the rewards to be well worth the effort.

To learn more about big data in the financial services sector, download our ebook, "MapR Industry Guide to Big Data in Financial Services." And stay tuned for the next post on how financial services organizations are harnessing data to stay ahead of regulators.

This blog post was published January 29, 2018.

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