10 Data and Analytics Trends Banks Should Consider

By Christopher J. Sifter
10 Data and Analytics Trends Banks Should Consider
As customer expectations change and regulatory priorities evolve, many of today’s leading banks are investing in new data technology solutions that have the potential to deliver significant cost savings, improve customer service, and help them gain or retain a competitive advantage.

But every new opportunity also introduces new challenges that must be considered and addressed. Following is an overview of some of the major data- and analytics-related trends that are affecting the industry today.

Industry context – what’s driving the trends
The competitive, regulatory, and technological landscape in the overall financial services sector has evolved rapidly in recent years. Key drivers include changing customer expectations – including a growing sensitivity to fees and a much higher demand for convenience and accessibility such as advanced mobile applications.

Competition from financial technology (fintech) companies helped accelerate these changes, and at the same time banks found themselves under continuing pressure to lower costs and improve overall efficiency and effectiveness. In response to these pressures, the nation’s largest banks have been working aggressively in recent years to take advantage of fast-changing technological advances.

Regional and community banks, on the other hand, often find they must allocate their more limited resources carefully in order to achieve the greatest possible benefit for their technology investments. As a result, the rate at which banks are adopting various advanced data science tools varies widely among individual institutions.

For example, when bank executives in a recent Crowe webinar were asked to characterize their institution's adoption of advanced data science technologies and techniques, more than a quarter (27 percent) of those responding said their bank was still focused primarily on the basics, using data for historical reporting and simple visualizations of trends. The majority (59.3 percent) said they were at various intermediate stages, either exploring their options or implementing some analytics projects (Exhibit 1).
exhibit 1
A relatively small portion of the respondents (10.9 percent) said they were starting to get into more advanced capabilities such as predictive and prescriptive analytics, and an even smaller group (2.8 percent) said they actually had mature predictive and prescriptive analytics in place.

Top 10 data technology trends

For banking organizations that currently have data science projects at various stages of development, the most immediate challenge often is deciding where and how to allocate their available resources. Advanced data science applications offer promising benefits in a variety of areas including marketing and sales, operations, customer intelligence, portfolio management, and risk and compliance.

Establishing priorities among such widely varied and important programs can be difficult. Here are 10 significant areas of concern that banks should consider as they evaluate their technology priorities:

1. Artificial intelligence (AI) and machine learning adoption. The banking industry’s adoption of advanced data analytics tools has begun accelerating in recent years as a growing number of institutions have come to recognize the potential benefits of such tools. Broadly speaking, industry leaders have demonstrated a number of ways in which AI – the ability of a machine to perform cognitive tasks such as problem-solving, learning, perceiving, and reasoning – can enable faster, more consistent decision-making.

More specifically, one particular form of artificial intelligence, machine learning, uses self-adaptive algorithms to identify patterns in data, which then can be used to make predictions about the probability of certain actions, such as customer default or early repayment. At an even more advanced level, deep learning technology uses algorithms that attempt to mimic the human brain by using hierarchical layers. This capability is particularly useful in performing specific tasks, such as customer segmentation, involving large amounts of data.

With other recent advances such as natural language processing and image recognition, leading banks are identifying potential applications in nearly every area of the bank, including operations, customer intelligence, sales and marketing, portfolio management, and risk and compliance. As a result, the industry is likely to see increasing levels of investment in both the technology and the people needed to take advantage of these capabilities.

2. Data warehouses and data lakes – avoiding the data swamp. Despite some observers’ contention that a centralized data warehouse is an outdated concept, for most banking organizations this remains a critically needed function. Big data and nimble fintech competitors did not eliminate the need for a centralized, trusted data source within an organization. In most instances, the move to so-called data lakes only shifted the governance burden – creating “data swamps” in which there are numerous islands of uncontrolled data.

The lack of a single trusted source of data inevitably leads to the common practice of individuals maintaining their own personal copies of frequently used financial or customer data, or developing their own spreadsheets to perform specific analyses. Such data islands or silos dramatically increase risk – and typically generate performance issues as well.

3. Customer experience taking center stage. Today’s most successful banking organizations place a high priority on customer-centric initiatives, such as the use of predictive models that anticipate customer behavior and enable greater personalization and responsiveness. As such, customer relationship management (CRM) is back in the spotlight, especially in the face of increased emphasis on digital strategy and customer experience.

The concept of a 360-degree view of the customer relationship has broadened, however. In addition to encompassing all of a customer’s accounts and interactions with the bank, today’s 360-degree view also must incorporate a social media presence. To remain competitive, banks cannot be satisfied with a digital strategy or a CRM approach that is merely adequate.

4. Cloud adoption increasing. The use of cloud technology continues to expand, with more and more organizations using cloud servers rather than local hardware to host both data storage and business processes. Privacy concerns are less of a barrier than they were in earlier years, but the use of outsourced processes creates a more complex computing environment, with increased emphasis on the control of data lineage and management of third-party risks.

5. Blockchain advances turning the corner. Tech companies are investing heavily in developing blockchain technology, but the adoption of such distributed ledger tools is still in the early stages within the financial services industry. Blockchain offers great promise in areas such as fraud reduction, improved know-your-customer and customer due diligence processes, and the use of smart contracts for handling payments and other transactions.

The next 12 months could be a watershed year as many in the industry watch to see how the largest banks work to develop a truly scalable and secure global infrastructure, while also waiting to see the role that regulatory oversight will play.

6. Cybersecurity as a continuing priority. A 2018 study by ISACA, an international professional association focused on IT governance, found that, for the fourth year in a row, cyberattacks continued to increase, but the methods used to combat them remained relatively static.1 Cybersecurity is a fast-changing area of concern, with a continuing need to develop solutions that anticipate what cybercriminals might attempt as they look for the path of least resistance to sensitive information. What works today might not be effective in six months.

It is also important to remember that cybersecurity is not solely an information security function or responsibility – all departments and functions need to understand their role in managing and protecting confidential data.

7. Evolving data privacy regulations. The European Union’s General Data Protection Regulation and the U.S. Gramm-Leach-Bliley Act remain the most prominent regulations governing how financial institutions share and protect customers' private information, but more recent regulatory actions by states, such as the California Consumer Privacy Act, are adding further complications. There no doubt will be more regulatory revisions from other states in the near future, so banks need to continually review and evaluate their data privacy policies and practices.

8. Sharper focus on data risk and managing dark data. In its most basic form, “dark data” is data that is retained by an organization but not actively used, mined, or governed. While such data could present additional opportunities for useful intelligence, it also increases banks’ risk exposure, particularly in view of some of the other trends already noted, such as increased adoption of AI and greater reliance on cloud technology. As a result, data risk is likely to be an area of increased focus and investment, with the need for banks to actively monitor the use of nonquality data for critical decisions.

9. Audit’s evolving role. One of the questions growing out of the adoption of AI, machine learning, blockchain, robotic process automation, and other types of advanced technology is how audit committees and internal audit departments will adapt their policies and procedures in order to continue fulfilling their oversight responsibilities. In late 2018, the Center for Audit Quality published “Emerging Technologies: An Oversight Tool for Audit Committees,”2 which provides a framework and questions to help guide audit committees as these emerging technologies take hold.

10. Increased emphasis on data governance. As technology advances, and as regulatory and financial reporting requirements continue to evolve, the risks associated with untrusted, ungoverned, and uncontrolled data will continue to increase. The need for a trusted, single source of data with strong governance and control has never been greater, especially when data is being used for purposes for which it was not originally intended.

Key data governance requirements include the ability to audit the full life cycle of data, from collection to consumption, including oversight of such basic issues as data lineage, access, controls, and transformation. A commitment to data governance should be manifested in organizational features such as a data governance board or committee, the appointment of a chief data officer or chief protection officer, and an increased audit focus on data repositories.

Why data governance is so critical

In addition to being critical to effective risk management, strong data governance also contributes to improved bank performance and competitiveness. For example, as banks address profitability challenges, the ability to make sound, objective, fact-based decisions is increasingly important. Having ready access to reliable, up-to-date performance data is essential to reducing unit costs and improving operating efficiency. The underlying foundation for such access is a sound data governance program, which will enable users to trust that the data that is collected, reported, and analyzed is accurate – without question or hesitation.

In the same way, some of the fundamental challenges banks must address in order to develop better customer insight also revolve around the data itself. Taking advantage of the rapid advances in AI and improved data analytics will be imperative for banks to remain competitive. Trusted, accessible data is the essential fuel that will drive this effort as well.

While most banks these days are relatively mature in terms of their IT infrastructures and the ways that they handle the rollout of new product or new software applications, the same levels of scrutiny and control often are not applied to the data itself. This is another example of when data governance becomes crucially important.

The objective of data governance is to see to it that data is managed as a business-critical organizational asset, comparable to an organizational utility. Just as users turn on a light switch without thinking about how the power is delivered or whether the light will be reliable, they also should be able to access data with similar confidence.

Industry experience suggests many banks still have some work ahead of them before achieving that level of performance, however. For example, when bank executives in the webinar referenced earlier were asked about their institution’s data governance practices and principles, fewer than a quarter of the respondents (23 percent) said they had established board-level data governance responsibilities, and had the necessary policies, procedures, and enabling tools in place. In fact, almost as many (18.5 percent) were at the other end of the spectrum, just getting started with improved data governance practices and principles (Exhibit 2).
exhibit 2

Today’s rapid technological advances, coupled with the sheer volume of data involved, can make developing effective data governance seem like an overwhelming challenge. The specific issues, priorities, and solutions are constantly changing, which is one reason banks of all sizes should regularly reassess and, if necessary, enhance their data governance programs. Getting started now is essential, even if the effort must begin on a smaller scale, and even if mistakes are made during the process. When it comes to improving data governance, the biggest mistake is to delay the effort.

As competitive pressures mount and regulatory priorities continue to evolve, banks inevitably will need to make additional investments in new data technology systems and solutions. By recognizing and understanding today’s top data trends, management teams will be better able to prioritize the various projects they are considering and do a better job of allocating their available time, money, and resources in ways that maximize the potential benefits these advanced solutions can offer.


1 “State of Cybersecurity 2018, Part 2,” ISACA, June 2018 https://cybersecurity.isaca.org/csx-resources/state-of-cybersecurity-2018-part-2
“Emerging Technologies: An Oversight Tool for Audit Committees,” Center for Audit Quality, Dec. 12, 2018, https://www.thecaq.org/emerging-technologies-oversight-tool-audit-committees


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