How the Financial Services Industry Is Winning with Big Data

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8 min read

Big data technology has become an integral part of the financial services industry, and will continue to drive innovation well into the future. Financial firms today can leverage big data to:

  • Generate new revenue streams through data-driven offers, such as personalized recommendations
  • Become more efficient in order to compete with FinTech companies, which use cutting-edge technology to provide customers with banking and financial services
  • Provide better services to customers, such as strengthened security

In my last post, I talked about how data is disrupting the financial services industry. Here, I'll discuss the growth of big data, how some of the most successful companies are using it to their advantage and steps you can take to leverage big data in your business.

The Rapid Rise of Big Data and FinTech

While some financial services organizations may be resistant to change, the fact remains that big data is here to stay. On a worldwide scale, more and more companies are purchasing big data and business analytics (BDA) solutions: IDC reports that worldwide revenues for big data and business analytics will surpass $203 billion in 2020.

In the same report, it was revealed that the banking industry is one of the top five biggest drivers of this growth.

Top Industry Based on 2016 Market Share

FinTech companies are handling this by using big data to offer unparalleled levels of convenience. They're taking away a sizable chunk of traditional financial firms' revenue by eliminating friction for customers-for example, payment companies such as PayPal and Upstart now allow customers to easily make payments online or participate in peer-to-peer lending.

By contrast, traditional financial firms lack the infrastructure to support those types of services, and are unable to understand how customers are using specific applications. They rely on things like surveys rather than real-time data to discover how customers are responding to their services.

The bottom line is that unless traditional financial firms can catch up to FinTech companies in terms of big data, they will not survive.

Forward-Thinking Companies Are Winning with Big Data

While traditional financial firms are falling behind, a few forward-thinking companies have taken steps to make the most of big data, and have saved a great deal of money in the process. Both of the companies we'll explore in this section have harnessed the power of a converged data platform to help them win with data. A converged data platform breaks down data silos by allowing organizations to store, organize and process all their data within a system. This leads to faster innovation, collaboration and access to data.

Case Study: An American Multinational Financial Services Company

As one of the largest financial services companies in the United States, this innovative company has more than 100 million cardholders. With the help of a converged data platform that facilitated machine learning, the company has benefited both customers and its business.

After realizing that traditional databases could no longer handle the amount of data and breadth of analytics needed to improve services and manage risk, the company set out on a multi-year mission to incorporate a big data infrastructure with machine learning directly into its business.

Their journey into big data began with a focus on risk management. By processing larger volumes of data from an array of sources on a converged data platform, they were better able to identify potential fraud, resulting in company-wide savings of over $2 billion each year.

In addition, the company leveraged machine learning techniques to connect card members and merchants through customized recommendations. And, since machine learning algorithms are continuously learning and improving, the company is capable of providing customers with the most relevant offers possible.

MapR Data Platform

Case Study: TransUnion

TransUnion, one of the top three credit bureaus, provides credit information, information management services and analytics to roughly 45,000 businesses and 500 million consumers worldwide. When they realized that their legacy platforms would be incapable of achieving the company's goals of digital transformation and enhanced innovation, TransUnion looked to modernize its data platform in order to provide its analysts and data scientists with greater flexibility in accessing all data and discovering insights.

The success of this self-service intelligence encouraged TransUnion to create a new line of business: to offer market insights directly to their customers and improve customer engagement. By doing so, they enabled their customers to make smarter decisions and develop improved risk strategies. One example was how large banks could see the curve of delinquency rates based on the origination of loans and acquisition rates over time. They could also do a peer analysis to see their delinquency rates versus their peers.

What Now?

With all that can be accomplished with the help of big data and the right data platform to harness it, it's time to make a plan and act on it. To make the most of big data, you should focus on three major steps.

1. Define a data strategy and align it with business goals

Although many companies strive to be data-driven, not all are successful. One common thread connecting companies struggling with big data is a short-term focus. Instead of looking at data strategically, they carry out one-off projects.

To avoid this, companies need to use data not just to accomplish singular goals, but also to accomplish their broader business goals. By developing a comprehensive strategy that spans across all departments as well as their network of partners, they can move forward with data and grow along with it.

2. Select a data platform that's flexible, scalable and secure for your business needs

No matter what platform a company is using, it's essential that it be flexible, scalable and secure.

Flexible and scalable platforms allow companies to collect and store as much data as they need while processing the data in real-time. Security means things like role-based access, which ensures that valuable information doesn't fall into the wrong hands, and allows companies to track data usage on a granular level.

3. Start with one business problem and expand on it

Earlier, I mentioned how an American multinational financial services company began to leverage the power of big data by using it to improve fraud detection. Once they had successfully done so, they moved onto other data-driven efforts.

This is a prime example of how companies can identify one business problem, solve it with data and then expand on that solution to address other issues. In this way, they break up the overwhelming endeavor of using big data into bite-sized chunks that can be easily built upon in the future.

It's clear that big data is a fundamental element of today's financial services industry, and the success of financial companies will be determined by their ability to harness big data in a big way.

To learn more about big data and its applications in the world of financial services, download the "MapR Industry Guide to Big Data in Financial Services" ebook. And stay tuned for the next post on the challenges and opportunities of data in the banking sector.

This blog post was published January 25, 2018.

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