Big Data in Financial Services: Driving Customer Experience and Business Value

A. Charles Thomas, Chief Data Officer, General Motors [NYSE:GM]
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A. Charles Thomas, Chief Data Officer, General Motors [NYSE:GM]

A. Charles Thomas, Chief Data Officer, General Motors [NYSE:GM]

Data practitioners often refer to the “three Vs” of big data – volume, variety, and velocity – but overlook a key fourth element: value. We are not in the business of simply producing data. Rather, we’re in the business of using meaningful information and analytic insights gleaned from data to optimize customer experience, reduce risk and drive process efficiencies.

When it comes to moving beyond big data to smart data that drives value and transforms the business, there are lessons retail banks can learn from other industries and from some of the newer companies in financial services. To the extent that banks want to emulate that technology, choice and timing in technology and process adoption is critical and really can make the difference in which providers emerge with a competitive advantage.

That said, traditional banks are not necessarily lagging in mastery of the use of data. If the emphasis is on technology and data alone, companies may be missing the big picture. Data and analytics are not just about data – it’s about making data relevant by using it faster and more effectively to proactively solve customer and business problems. Big data applications and analytic insights and talent are leading to remarkable work in several key areas in financial services.

First, data and analytics help us better listen to and deliver for our customers. In the age of e-commerce, there is increased pressure on traditional banks to deliver greater value and an optimized experience to an information-savvy customer base that expects “digital-native” ease of use for all financial services. Gleaning insights from real-time customer events, through the ability to tap into real-time data streams before the data are archived and stored in a traditional data warehouse, is critical and helps move analytics closer to the point of customer interaction. Additionally, analytics on unstructured data, such as images, text and voice, is emerging as a key area for banks to understand customer needs and sentiment in a more nuanced manner. This is facilitated through increased use of advanced machine learning algorithms, like deep learning, and analytics of “messy data”– again, before the data show up in traditional analytic data warehouses.

 Focusing on practical, real-world applications, always with a customer-centric perspective, will be a critical differentiator 

Second, data and analytics opportunities in the fraud detection space ultimately benefit and protect both customers and banks. The financial and technology industries are innovating and converging to offer more security and accessibility for consumers. Opportunities here range from text and speech analytics, to machine learning techniques such as adaptive algorithms for anomaly detection, and use of biometrics to access account data and other services through secure voice, face, eye, and fingerprint identification. Of all the promising aspects of artificial intelligence, a branch of machine learning called deep learning is one of the areas most likely to make a short-term impact here. These methods are being put to good use today in some operational areas, but there’s so much more opportunity. For example, if an adaptive algorithm can stay one step ahead of fraudsters by detecting anomalies across many different event types, and then detecting the way the anomalies themselves are changing, there’s opportunity to find creative uses for that power elsewhere.

Lastly, there continues to be a great deal of unrealized opportunity on the efficiency side. Our team recently developed an analytic solution enabling a business group to do something in less than a day that would have taken years to accomplish in the traditional way. Years. It’s a great application of text analytics to unstructured data and will be increasingly prevalent. Machine-based analytics are going to start taking over aspects of data and analytics that previously have been un-automatable. That will turn management reporting processes upside-down, and transform what it means to be a database administrator, and what it takes to develop and manage portfolios of statistical models.

Foundation to all of these data and analytics technologies, tools, and opportunities are the talented people doing the work. It’s critical to elevate the practice of analytics to allow us to attract, cultivate and retain the best, most creative analytic professionals. After all, it will be these team members who develop the next innovative solution to drive business value and enhance the customer experience.

It’s an exciting time to be in data and analytics, particularly in financial services. Focusing on practical, real-world applications, always with a customer-centric perspective, will be a critical differentiator over the next several years.

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