Banking on Big Data

New data from new sources is transforming financial services.

Banking on Big Data
The financial services industry was relying on vast quantities of information long before it was called big data. Yet big data has never been more important to banks, insurers and advisory firms than it is now.

Across industries, investment in big data and analytics will increase by 12 percent year over year to exceed $210 billion by 2020. But no industry will invest more, or increase spending more quickly, than financial services, according to IDC®.1

Financial services companies are combining traditional transactional data with new information streams. Goals include improved efficiencies, compliance and customer experience. But to achieve those outcomes, banks and insurers will need to make changes — in aligning IT with the business, applying advanced analytics and delivering the right data to the right people.

New Data, New Firehoses

Traditional data sources offer a limited view of the market. Smart financial services companies are combining this information with new streams, including:

  • Internet of Things (IoT) — Insurance has been a leader in deploying IoT for usage-based auto coverage. But there are other opportunities. Internet-connected household items that trigger automatic purchases can create cardholder buying profiles for better fraud detection. Location services can enable real-time financing for shoppers of big-ticket items. In the branch, IoT beacons can help banks understand how customers consume services and give them a personalized experience at kiosks or video tellers.
  • Social media — Eighty-four percent of consumers are active on social networks, and 74 percent of millennials prefer social media to the phone for customer service.2 Financial services companies can apply sentiment analysis to social media interactions to understand customer perceptions and desires. Banks may be able to identify customers in the market for cars or homes, for example, and present relevant offers. Insurers can factor in social media data in customer risk profiles. Advisory firms can leverage social media ads to reach key demographics. Of course, some of these activities raise privacy issues, and companies will want to proceed with caution.
  • Employee data — One source of big data that companies often overlook is employee consumption of IT services and support. Service desk data, effectively analyzed, can provide invaluable insights into how successfully employees are using IT to get their jobs done. End-user data can help companies improve employee productivity and satisfaction while optimizing the use of IT for better customer service. 

The More Things Change

Figure 1: Type of Analysis That Informs Major Decisions
Big data opportunities notwithstanding, many financial services companies still rely more on human judgment. (See Figure 1.) To better leverage big data, financial services companies should take four key steps:
  1. Align IT with the business — IT can’t be expected to understand which data streams the business needs to capture and analyze. The business probably doesn’t have a complete understanding of what’s technically feasible. So, the two must work together. In fact, cooperation between IT and the business is the number one success factor when it comes to big data initiatives.4
  2. Move data to the cloud — Top-tier banks will leverage the cloud to slash infrastructure spending by one-quarter by 2019.5 But the cloud also offers a flexible, central repository for harmonizing transactional data locked up in legacy systems with data streams from IoT and other sources. What’s important is to work with a cloud provider that understands the unique business and security needs of financial services companies.
  3. Apply advanced analytics — As data volumes increase, banks and insurers will rely more on automation and machine learning to analyze data and generate actionable information. Automation will help them generate reports in real time. It can also streamline processes such as underwriting and reconciliation. Advanced algorithms and machine learning will improve fraud detection, risk management and even financial services marketing.
  4. Deliver the right data to the right people — Finally, you need to get your big data insights to the executives and staff who can use them. That probably calls for visualization software that allows stakeholders to quickly and easily consume information. End-user personas can also help. Personas categorize end users into groups with similar needs for devices, applications and data. 

Data-Driven

More than 80 percent of organizations say their big data investments have been generally successful, according to a survey that sampled primarily financial services companies.6

Figure 2: Big Data Initiatives and Results
Not surprisingly, the level of success varies by intended outcome. (See Figure 2.) In financial services, many companies are looking at three big data objectives:
  1. Lower costs, higher efficiencies — Big data can dramatically speed routine processes such as credit decisions and trade settlements. It can also maximize employee productivity. It can even extend to real-estate management, allowing banks to match customer demographics with real-estate costs to precisely locate branches, kiosks and other touch points.
  2. Better risk and compliance — Usage-based insurance will account for 15 percent of auto insurance and 10 percent of home insurance by 2019.8 Behavioral analytics are already in place in 15 percent of banks to help avoid regulatory fines and sanctions.9 And data captured through security information and event management (SIEM) tools can help identify and resolve potential issues before they result in a data breach.
  3. Exceptional customer experience — Big data can help you attract customers by uncovering their desires, retain customers by identifying those at risk of leaving, and expand wallet share through personalized cross-selling and upselling. What’s important is to be specific about what you want to achieve — whether it’s faster customer processing, self-service tools, consistent experience across channels or better-equipped employees who can serve customers better.

Big data is never an end to itself. Rather, it gives you insights to help you achieve your business goals. But whether you hope to track employee IT consumption to improve productivity, understand customer behavior to improve customer experience or gauge customer preferences to drive new revenues, big data is set to transform the business of financial services.

1 “Big Data and Business Analytics Revenues Forecast to Reach $150.8 Billion This Year, Led by Banking and Manufacturing Investments, According to IDC,” IDC, March 2017
2 “Digital Democracy Survey 2016,” Deloitte, 2017
3 “Top Financial Services Issues of 2017: Thriving in Uncertain Times,” PwC, December 2016
4 “Big Data Executive Survey 2016,” New Vantage Partners, 2016
5 “IDC Futurescapes: Worldwide Financial Services 2017 Predictions,” IDC, November 2016
6,7 “Big Data Executive Survey 2017,” New Vantage Partners, January 2017
8,9 “IDC Futurescapes: Worldwide Financial Services 2017 Predictions,” IDC, November 2016

Deloitte® is a registered trademark of Deloitte Touche Tohmatsu. IDC® is a registered trademark of International Data Group, Inc. New Vantage Partners® is a registered trademark of New Vantage Partners LLC. PwC® is a registered trademark of the Trustees of the PwC Business Trust.

All data cited in this article is used by permission.

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