How Banks Leverage Big Data for Big Value

How Banks Leverage Big Data for Big Value

Posted on August 19, 2016



Technology professionals in the banking industry face a wide range of concerns due to the nature of the data stored within their systems. The potential reward in stealing or compromising financial data makes these organizations a prime target for cybercrime, while stringent industry regulations require security to be extremely tight at all times.

Is it possible to strike a balance between defensive, regulatory, and data access needs and still provide access to data for analytics without compromising the security of the entire system? The truth is that many organizations in the financial sector have deployed analytics technology while being able to keep their security tight, and are benefiting from it.


How Banks are Using Analytics

While perhaps the most visible analytics deployments happen in marketing and customer experience departments, there are numerous cases in which this technology can be used to make smarter decisions, such as fraud detection, operational performance tracking, and more. In an April 2013 interview, McKinsey director Toos Daruvala highlighted the ways in which banks are currently using analytics to reduce risk and improve their revenues. He highlighted the area of risk assessment in particular, noting that the U.S. financial crisis in 2007 has caused many financial services institutions to reevaluate their discriminatory models. One bank, for instance, had not revised its risk assessment models in several years. This had hindered their ability to distinguish good and bad risks between 40 and 45 percent. Although they had all the right information to create a comprehensive view of their customers, the relevant data existed in separated silos. By bringing this information together in a comprehensive data analytics project, the bank increased their accuracy to 75 percent.

“What you see is that almost every single major decision to drive revenue, to control costs, or to mitigate risks can be infused with data and analytics,” Daruvala said.

While banks can potentially gain a competitive advantage from applying analytics to inform a broad range of decisions, not all of these paths lead to success. As a result, it is essential that decision makers in the financial services sector become cognizant of the barriers that often stand in their way.


Roadblocks on the Path to Insight

As the McKinsey examples illustrate, analytics can provide significant value even when the technology is implemented in just a single area of business. However, a July 2013 survey conducted by Deloitte confirmed that a significant number of banks are not using data as well as they could be. One of the issues researchers discovered was the lack of data integration. In fact, 27 percent of bankers said their data was not integrated. Furthermore, only 30 percent of respondents rated the quality of their data or information as “adequate.” 

A lack of data integration can have a far-reaching impact on the organization’s goals. Analytics software is only as powerful as the data that it draws on. As a result, incomplete data or an insufficient pool of information will make data quality suffer and can lead to poorly informed decisions.

Organizations must also ensure that their analysis tools are well equipped to display information meaningfully, and they have the infrastructure to support analytics activities.

It is not only the lack of integration that makes analytics projects suffer, however. Organizations must also ensure that their analysis tools are well equipped to display information meaningfully, and they have the infrastructure to support analytics activities. Deloitte highlighted several areas that banks have struggled with in achieving their analytics outcomes. One of the most prevalent issues is the way that data is managed. For example, many organizations still rely on spreadsheets for complex data sets which is not only time-consuming, but compromises data accuracy due to the number of manual tasks required for revision. Other common challenges include:

  • Reporting tools that offer limited analytics capabilities
  • Lack of IT infrastructure to support analytics
  • Limited options for analyzing unstructured data
  • Lack of staff with analytics skills

There is a need to think of data analytics more strategically by investing in tools that cover the full range of processes that go into such initiatives— from the infrastructure used to store the information to the reporting tools required to convey it to key business and IT stakeholders. One of the broader areas identified for improvement is the speed at which employees can make data-based decisions. Deloitte principal, Omer Sohail, emphasized the value of real-time analytics, suggesting that sophisticated solutions must be able to deal with numerous sources of real-time information at once. However, the value of real-time analytics capabilities will be diminished if other core issues such as data quality and unstructured information are not addressed.

Relying on tools with only basic functionality will result in suboptimal data analytics deployments as information environments become more complex. There is a growing demand to make data accessible, even to users without technical knowledge. Maximizing the value of data access and analytics will most likely require a shift in business processes and strategies. According to an Accenture report, this is due to the fact that much of the value in analytics comes from the technology’s ability to make trends and connections more obvious. It is important to consider experimentation, whether it is in the form of establishing new business success metrics or using analytics to follow the company’s performance and identify weak points. Organizations that adopt a perspective of seeing data as a source of innovation stand to gain a competitive edge when it comes to forming new ideas.

For example, Accenture found that the number of businesses that rely on data to generate new ideas doubled between 2009 and 2012. Among top performers, respondents cited several primary advantages of a data-driven approach: 

  • Improved operational efficiency 
  • Increased productivity
  • Faster decision making

“With business operations accelerating and decision-making occurring with real-time immediacy, the weighting of these tools will inevitably need to shift in favor of data-based approaches,” the report stated. “Respondents note that the ability to react quickly to changes in the market yields rewards in terms of new business and customer satisfaction. These executives say that as market dynamics change, their use of analytics will help them drive more value for the business by providing data that is easy for department heads to understand, and therefore more helpful in making decisions.”

There is huge growth potential in adopting a data-driven mindset. However, the financial services industry faces the unique challenges associated with a highly regulated environment. Banks can’t avoid pursuing some avenues simply because they present an unacceptable level of risk. Analytics-driven insight must not be kept away from these organizations. Instead, there are technical solutions that solve the problems of data complexity as well as those associated with security.


Shifting to the Data-Driven Organization: Technical Tools

While IT professionals may be hesitant to let the doors of enterprise data access wide open, they must ensure that employees can access the information that they need. The right solution would be able to provide streamlined, secure, real-time data access. Solutions such as Sequel provide the ideal solution to accessing AS/400 information, as well as other platforms (SQL Server, ORACLE, MySQL), by giving administrators an easy way to control who accesses their data. 

Designed for IBM i, Sequel works with the i5/OS object authority, making it easy to learn. Administrators can control who accesses particular sets of information and what they can do with it, down to the field level. Sequel also makes audits less painful by incorporating usage history and audit reporting tools into the system. This ensures that IT leaders always have a way to investigate potential issues such as unintended misuse or attempted data breaches.

In addition to empowering IT staff with the tools they need to control data access, Sequel solves the challenge of making data digestible. It give users the ability to create and customize dashboards so they can focus on the information that is most relevant to them. Whether executives need a summary of the company’s key performance indicators or IT administrators need a detailed view of collected system performance data, dashboards can make the right information readily available.

With Sequel, it is possible to strike a balance between security and regulatory needs while still providing access to data for analytics and insight, even in the financial sector.