Your Bank Needs a Secure Big Data Approach

Not long ago, Big Data was little more than a term bandied around by the technology, sales, and marketing communities. Today, this body of intelligence is used across virtually every sector, like banking, that integrates online and offline customer channels.

With continued exponential increases in data collection (50 times year over year), big data has become an accepted – and expected – part of a cohesive operational strategy. But it’s not without challenges. 

The HORNE Banking team, working in close collaboration with our in-house cyber solutions experts, has identified some of the main hurdles to the collection and use of Big Data for banks. For each, we have offered some things your bank can do to mitigate the challenges and turn your customer intelligence into an opportunity to build informed strategy in 2016.

Issue One: Small Data Sets

First and foremost, many companies – banks included – still use only a small fraction (somewhere between 5% and 12%) of the data they collect. This is due largely to lingering confusion about where the data is sourced, what forms it can take, and how to apply internal controls. Without an ability to prioritize the small amount of big data they are tapping into, banks risk missing the most useful intelligence available to them.

One of the first steps to putting Big Data to work is to clearly segment the two forms:

1. Unstructured Data: This information is mostly qualitative, meaning that it cannot be interpreted within traditional database models. Nonetheless, this type of data is accessible and useful for community banks that have active offline client relationships.
2. Multi-structured Data:  This is often a blend of qualitative and quantitative data that needs to be organized for usefulness.

Issue Two: Controls

As noted with regard to multi-structured data, the most useful Big Data sets are grouped into control categories. The recommended approach is to create four main segments:

1. Approach and Understanding: These are entity level controls or “tone at the top” controls over Big Data stores. They’re necessary for developing policies and procedures, governance systems, and data inventory management processes.
2. Quality: These controls help to ensure data accuracy, reliability, completeness and timeliness in accordance with company policy and industry standards.
3. Confidentiality and Privacy: These logical and physical access controls restrict unauthorized access to sensitive data. In our current cyber security environment, this is perhaps the most important set of controls for banks.
4. Availability: This last set of controls helps to make sure that the data is made available to the enterprise for business continuity and disaster recovery controls.

Issue Three: Risk

In the current digital banking environment, this may be the most significant of the hurdles to using Big Data. Banks handle large volumes of private customer information (like credit card numbers, bank account numbers, and personally identifiable information), as well as maintain strategic intelligence and vital performance information like sales figures, financial metrics and customer metrics. Any vulnerability is a risk to the enterprise.

And the continued uptake of mobile banking apps and digital banking mean that Big Data and cyber risk are one big tangled mess. While 55% of American smartphone owners made a mobile bank transaction between 2014 and 2015,[1] only 6% of smartphone owners say they trust mobile technologies for financial transactions.[2] And although the vast majority of consumers (86%) think their bank is doing enough to protect their apps, 41% of those same people expect those apps will be hacked.[3]

They might be right. Cybercriminals continue to produce malicious apps, often staying a step ahead of those who are trying to block them. Mitigating this issue requires vigilance and clear communication – proof that your customers can trust the tools because those digital engagements represent your broadest source of Big Data inflow 

Put Your Big Data to Use

Building a concerted Big Data approach is worth the effort. We recommend a few steps to engage your ever-growing amount of intelligence:

1. Identify business requirements for data analytics based on strategic goals.
2. Identify and train “Big Data Champions” in IT and various business units.
3. Clearly outline your data sources and align those resources to meeting their stakeholder needs.
4. Consistently assess the value of data against the associated costs of storage and retrieval, considering compliance, privacy and regulatory concerns.
5. Maintain a scalable budget for infrastructure and database analysis tools.
6. Create data sources and analytics, beginning with those that bring the highest value to the organization (increase customer retention, reduce customer acquisition risk, reduce fraud).
7. Maintain a repeatable process for acquisition and usage of data.

Understanding Big Data, its sources, uses and risk factors are all components of managing your information flow to create a successful customer engagement strategy. With a focus on cyber solutions specifically for banks, we can help you do this with security and confidence. Contact us to learn more. 

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[1] 2015 Verizon study

[2] MyBankTracker personal finance and bank ranking platform

[3] Arxan security software study