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Big Data in Retail Banking – The Opportunities and Challenges

Big Data

There’s a lot of buzz about big data in the retail banking sector right now as all the major banks work out how best to bring new unstructured data sets (such as social data and mobile data) together with transactional data in order to improve customer experience, become more competitive and drive growth.

I recently discovered this great debate from September 2012 that provides a great understanding of where banks are today in their use of big data. The video includes panelists from HSBC, Barclays and RBS and the full debate lasts for more than an hour.

For those of you without the time to spare to watch it all, here is my summary of the main points:

1. What are the pain points that banks are grappling with?

  • Customer retention, cross-selling, up-selling, developing new products that customers actually want, and minimising fraud

2. Where are the biggest opportunities with Big Data?

  • Improving insight and understanding of the customer in order to deliver a better customer experience through highly personalised communications (‘the segment of one’)
  • Using social media analytics to find out what your customers think of your competitors and their products
  • Identifying and reducing fraud. Part of this is showing fraudsters that you are looking for them. Most banks are doing real-time detection already and this is where Big Data, combined with social data, can come into its own

3. What are the challenges with Big Data?

  • Gaining permission to use and process some of the new data sets such as mobile and social media data. The panel all admit that financial services is behind the curve in this because of compliance issues, and that a lot could be learned from some of the new technologies and techniques that companies like Google, Facebook and Amazon have developed
  • The ultimate goal of Big Data should be about delivering a better customer experience for customers. Not easy when the user journey is now dynamic when it used to be confined to in-branch interactions
  • Finding the right balance between giving the right access to data across the company, and making sure adequate controls are in place. This is because the further away from source it gets, the harder it is to ensure compliance is maintained

4. Where should retail banks start in Big Data?

  • Think about who owns the customer and therefore the data relating to the customer. This will require a rethink in organisational and governance structures, and a real need to get the C-Suite bought in
  • Focus on your strategy in order to frame the right questions and therefore data that you need. There are infinite possibilities with Big Data. That said, the business and the data analysts need to work collaboratively. Once you start to visualise data, it can raise new questions or reveal that the original question wasn’t right in the first
  • The Holy Grail is to get the single view of the customer first, and then enrich this later with the newer data sets such as social data. Take things step-by-step – unlike Facebook, banks cannot afford to get their communications to customers wrong! Banks are already governed by a set of regulations to use data responsibly
  • The emphasis should be on quality and not necessarily speed of communications. The next best action for the customer may not be a cross-sell – that won’t drive loyalty or build trust

Comments on this Article: 2

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  1. Great segment. The panelists make some solid points and they are spot-on in saying that the overall goal of Big Data is to deliver a better customer experience. I thought it was interesting, however, that in discussing the need to understanding customers better, the panelists didn’t delve further into how this directly impacts customer churn—by learning customers’ interactions across multiple channels such as bank visits, calls to customer service departments, web-based transactions, mobile banking and social media interactions, etc., banks can detect early warning signs to predict churn. And this is precisely where Big Data comes in to play for banks—it is empowering banks to sift through the massive [and unstructured] volume of customer data to retrieve valuable insights to prevent churn and increase cross-sell. I recently examined the very same topic in a blog post (http://www.ngdata.com/site/blog/220-ng.html) that explored how banks can predict and prevent customer churn by leveraging Big Data. Understanding that the retail banking industry is challenged with compliance issues, Big Data technologies present a significant opportunities for banks and it will be interesting to see how they make the most out of their Big Data initiatives.

    –Naren Patil, NGDATA

  2. CyberH says:

    Helen, great article! With the explosion of big data, companies are faced with data challenges in three different areas. First, you know the type of results you want from your data but it’s computationally difficult to obtain. Second, you know the questions to ask but struggle with the answers and need to do data mining to help find those answers. And third is in the area of data exploration where you need to reveal the unknowns and look through the data for patterns and hidden relationships. The open source HPCC Systems big data processing platform can help companies with these challenges by deriving insights from massive data sets quick and simple. Designed by data scientists, it is a complete integrated solution from data ingestion and data processing to data delivery. More info at http://hpccsystems.com

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