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TDWI Upside - Where Data Means Business

How Banks Can Use Their Data to Compete with Fintech Start-ups

Big financial institutions are recognizing that years of data collection can be used to counter challenges from smaller financial start-ups.

Established financial institutions are on the verge of a major step forward with the help of recent technological improvements and lessons learned from financial technology (fintech) start-ups. Over the last decade, the emergence of fintech start-ups has stoked competition while the maturity of new technology platforms is allowing large institutions to rapidly upgrade their infrastructure and performance.

However, large financial institutions have an untapped resource: their data. These firms have just begun to realize the value of their decades of information about customer behavior and how they can use it to respond to disruption.

Changing the Mindset

In the past, major financial institutions were rooted in legacy infrastructure that served them well and generated profits daily. However, any tweak or upgrade often required a deliberate, incremental approach with an eye to security and governance -- a far cry from the jump-in-and-fail ethos of smaller start-ups.

Beyond the logistical hurdles, major financial institutions faced a larger philosophical barrier when they wanted to upgrade. The leaders of these firms have, understandably, emerged from a financial services -- not a technology -- background. This poses challenges when convincing decision makers to appreciate the value of investing in new technology when legacy systems lag behind industry gold standards but are not actually failing.

In recent years, financial institutions have significantly modernized key parts of the customer experience and have become front-runners in implementing secure smartphone apps and online resources as well as automating and streamlining customer service.

The competition has not been quiet. Recognizing the scale of the major players, smaller fintech players have entered the financial sector. Appealing to tech-savvy millennials and the underbanked, these technology-led start-ups are producing the kind of easy-to-use, online-first service that consumers now expect. Promising a superior user experience, these start-ups dangled the possibility of rapid disruption of the financial sector. The onus was on the large institutions: innovate if you want to maintain leadership.

Big Banks and Bigger Data

Overcoming obstacles to innovation has proven to be a slow burn for many companies. Nonetheless, financial institutions have made huge strides in recent years, learning from the competition and leveraging their scale to their advantage.

As open source software such as Hadoop and Spark has matured considerably over the last few years, banks have been able to make progress modernizing their IT and big data strategy. For years, major institutions have collected customer transaction data in real time. This real-time data, however, was aggregated in a familiar format -- the monthly statement.

Now applying mature data analysis techniques allows banks to gather data about spending habits, late fees, overage charges, and other behaviors. Financial institutions have an almost real-time understanding of customers and their habits. On a practical level, that leads to improved operations in such areas as marketing new credit card offers, fraud prevention, and risk analysis.

To be sure, this kind of data analysis is not easily achieved. Moving data from a legacy warehouse to a system based on Hadoop and Spark is a time-consuming process. However, financial institutions have mastered some of the skills pioneered by start-ups, including smaller teams working on specific projects as proof-of-concept to develop scalable solutions. The data migration is far from complete, but major institutions (including Barclaycard, the firm I work for) have shown that it can and should be done.

Looking to the Future

In years to come, the push into greater data analysis will only continue at major institutions and start-ups alike. Indeed, it can be argued that the most effective approach will be one of collaboration rather than competition. Start-ups have fresh, agile technology solutions with no legacy infrastructure. Financial institutions, by contrast, have the edge when it comes to mitigating risk, depth of data, and navigating regulatory requirements. Using their complimentary capabilities -- financial institutions working with, rather than against, start-ups -- could be the real key to success.

The advent of machine learning techniques will unlock further potential in financial services data. Start-ups and major institutions alike can collaborate on this important project -- to the benefit of all customers in the financial services industry.

About the Author

Shelton Shugar is CIO of Barclaycard US, where he brings more than 25 years of experience in enterprise computing, as well as deep knowledge in cloud computing, product development and e-commerce. Previously, he served as vice president, enterprise cloud services with Hewlett-Packard Enterprise in San Francisco. He has previously held positions at CA Technologies, Yahoo!, eBay, Homestore, Verisign, The Thomson Corporation, and AOL. He holds an M.S. in computer science from Johns Hopkins University.

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