Data Confidence in 2016: Five Trends That Require Action

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With the onslaught of big data and the Internet of Things, it’s hard to avoid the data revolution. This year will be no exception for banks and financial institutions as they continue to find themselves at the center of all things data.

One of the biggest issues they will face in navigating the data-driven world is achieving data confidence. Banks must continue to invest in programs that inspire the confidence of their customers, regulators, senior management, business partners, and investors. But to do this, their constituent groups must know the data they are providing to and consuming from the banks is accurate, timely, secure, and well-governed—the core requirements of data confidence.

Here are five trends that justify the prioritization of data in 2016 and beyond:

1. Ongoing regulatory expectations

Although regulatory compliance is a burdensome reality for financial institutions, regulations serve as an important guidepost to achieving data confidence. Since the financial crisis, regulators have focused on not only the final reports banks produce, but the data, processes, and technologies that drive them. Since the Federal Reserve started the Comprehensive Capital Analysis & Review (CCAR) as part of the Dodd-Frank amendment, more banks have run into trouble with the qualitative review (which looks at the processes and controls that support each bank’s capital planning process) than with the quantitative review, which looks at the capital levels of the bank. In 2015, three of the 31 banks reviewed by the Fed received a conditional or full objection based on qualitative concerns.

BCBS-239. In 2016, in addition to the CCAR qualitative review, large institutions that have been deemed systemically important banks by regulators will be required to comply with an element of Basel III entitled, “Principles for effective risk data aggregation and risk reporting,” more commonly known as BCBS-239.

Highlights of this regulation include requirements for:

  • Generating accurate and reliable risk data to meet normal and stress/crisis reporting accuracy requirements.
  • Aggregating data on a largely automated basis to minimize the probability of errors.
  • Generating aggregate risk data to meet a broad range of on-demand, ad hoc risk management reporting requests, including requests during stress/crisis situations, requests due to changing internal needs, and requests to meet supervisory queries.

Although this regulation currently applies only to the large systemically important banks, smaller national banks and regional banks should expect these principles to apply to them at some point in the not-so-distant future.

General data privacy regulation. A second regulation with potentially sweeping impact is the EU’s General Data Privacy Regulation. Although this may not become law until 2018, banks should get a head start on preparing for it this year. In its draft form, this regulation:

  • Applies to all businesses that offer products and services in the EU market. This includes the online activities of non-EU companies that offer goods or services to EU individuals.
  • Allows data-protection authorities to impose substantial fines for non-compliance. Fines are expected to rise to a maximum of 4% of a company's global annual revenue.
  • Maintains the general prohibition of data transfers to non-EU countries that are not officially recognized as “adequate,” but applies stricter conditions for obtaining “adequate” status. This is particularly important, as a recent EU Court of Justice’s decision invalidated the EU-US Safe Harbor framework for lack of “adequate” protection.
  • Codifies the “right to be forgotten” and creates a right to easily transfer personal data from one service or product to another (i.e., the “right to data portability”).
  • Requires companies to notify the relevant national supervisory authorities and affected individuals of serious data breaches, likely within three days of the breach.
  • Limits the use of data for profiling (i.e., modeling) and limits the enforceability of decisions made using automated decision technologies.

2. Proliferation of analytics

As organizations and individuals become more data aware, they are driving a growing need for analytics. Banks should address this trend on two levels.

Democratization. Software is making analytics available to less sophisticated users. This trend will continue, and conducting analysis on large datasets will become considerably simpler. Organizations have learned that data governance, when done well, helps nurture a culture of analytics that meets business needs. Banks must promote data confidence by giving analysts access to centralized, clean, easy-to-understand data sources.

Third-party support. Because we are facing a skills gap in the data scientist market, banks face the challenge of leveraging their data while managing their investment in human resources. We are likely to see more complex analysis efforts outsourced to companies with the expertise and the resources to maintain large pools of quantitative experts.

3. New and better data governance

As more employees get self-service access to data, there is a growing risk of creating many data and report instances, leading to numerous data sources beyond the control of data governance. With data discovery tools, there is a risk of losing control over analytics deployments, which require new ways to govern and control data. Data governance will be a key trend for 2016 as banks execute initiatives to better govern the quality, lineage, access, and distribution of data.

4. New approaches to fraud detection

As fraudsters take advantage of new technology, 2016 will be the year that advanced machine learning becomes an important tool in the fight against fraud. Machine learning helps banks process large volumes and varieties of data to identify patterns and markers of fraud. In its report “Top 10 Strategic Technology Trends for 2016,” Gartner identified advanced machine learning as one technology that will impact organizations’ long-term plans, programs, and initiatives. According to Gartner, “the explosion of data sources and complexity of information makes manual classification and analysis infeasible and uneconomic.”

5. Emerging relationship between banking and fintech

We regularly see headlines highlighting the disruption of the banking industry through the efforts of a growing number of fintech startups. A closer look at this competitive landscape shows that while fintechs are unconstrained by legacy platforms and complex bureaucracies that limit traditional banks, they need the large customer bases, access to capital, and ability to fund growth that banks possess. This is leading to an emerging symbiotic relationship between banks and fintechs. Data confidence will be critical to the success of these relationships as organizations seek to leverage the strengths of each partner while overcoming the limitations of the combined entity.

Data underlies all the channels, applications, processes, and tools of today’s digital world. For banks in particular, the collection, management, and use of data is at the core of their relationships with customers. Banks that direct their efforts—from compliance to marketing—toward building data confidence are the ones most likely to establish deep and lasting trust with their customers. And this is the ultimate key to leading the data revolution.

Post Date: 2/5/2016

Michael Goodman, NTT DATA Services NEW Michael Goodman

About the author

As Vice President of Business Insights, Michael brings a mix of analytics, data, technology, financial institution, and consulting experience to NTT DATA Services. He works with individual clients to identify how NTT DATA can improve client capability and performance, while helping ensure that optimal practices and resources are applied to each client opportunity.