How Big Data has revolutionised the finance industry

Few innovations have impacted the finance industry like the emergence of Big Data.

Long gone are the days of customers walking into their local bank branch and dealing with all their banking needs with the help of a cashier. Now, clients access a diverse range of banking products through smartphone apps and online banking, as well as using conventional in-branch services. 

The banking sector, much like the rest of the global economy, underwent a fundamental transformation with the rise of the internet and social media. Countless data sources are now available to financial service companies that they can analyse to better understand their customers and offer them more personalised services and products. 

As customers continue to create huge amounts of data, in both a structured and unstructured form, whenever they apply for a loan, reduce their credit limit, make a major purchase or buy online, Big Data analytics tools are being deployed to both understand and develop actionable insights. 

For example, Bank of America was one of the first financial organisations to use social media data to identify customer service issues that have the potential to damage customer retention.  

When the bank used Big Data mining to analyse over 41,000 comments on Twitter and Facebook, they found thousands of comments about false rumours about purchase limits that placed the bank at risk of losing customers. This ability to find customer issues quickly before they grow and cause even more dissatisfaction is a powerful tool offered by Big Data technologies. 

 Companies in the finance industry use a range of Big Data technologies, including artificial intelligence (AI), machine learning and natural language processing. When used effectively, Big Data technologies can process complex data sets to unlock value in customer data that humans would simply be unable to process. In an increasingly competitive industry, it’s vital for companies to adopt cutting-edge technologies to gain a competitive edge. 

A Capgemini survey found that over 60% of financial institutions believe that Big Data analytics provide a significant competitive advantage. More than 90% of financial institutions also believe that successful Big Data initiatives will determine the winners of the future. 

Lockdown and social distancing rules have placed a renewed burden on the digital services offered by financial institutions to be available for customers to undertake a range of banking activities. While the shift to digital is not a new phenomena, the impacts of the pandemic have sped up the migration to banking from anywhere. 

As physical branches reduced their hours or temporarily closed, payment extension requests, new account openings and loan applications have all moved online. Without using Big Data tools, banks would have quickly become overwhelmed by the massive number of applications and resulting bottlenecks. Customers that had to wait too long for a response on their application would simply move their business to a bank that offers better customer service. 

However, banks still need to ensure a large number of factors are assessed before offering credit to a customer or approving a loan. By feeding relevant customer data into Big Data technologies, banks can speed up this process, at the same time as improving risk management. The more data credit risk management solutions have about a customer, the more accurate the credit scoring will be. 

Incidences of fraud have become increasingly difficult to detect as customer service interactions shift away from face-to-face transactions. HSBC used machine learning and AI to better discover potential fraud in a variety of ways including checking IP addresses and automatically flagging transactions that appear to be out of character for the customer for further security checks. 

But customer service is the main area HSBC are deploying Big Data technologies. During the pandemic when phone lines were overloaded with callers, chatbots became a core communication method for many customers. Using Natural language processing (NLP) technology, chatbots are able to translate text and link it to previously established patterns to provide relevant answers. More advanced chatbots are able to assess context and understand the sentiment of the client to catch frustrations and issues early on.  

The text input can then be fed through machine learning tools to seek out common concerns or challenges customers are facing. In the future HSBC aim to expand the ways customers can use chatbots and incorporate more personalised offers and products through this channel.   

Standard Chartered Bank is using Big Data to gain deeper insights into customers behaviours and target them with hyper-personalised offers and deals. With the real-time data analysis offered by analytical software, valuable insights can be extracted from typical transactions.  

For example, Standard Chartered Bank uses the case of a shopper who has a history of buying high-end clothing, jewellery and coffee as a prime target for Big Data advances. The bank can see the history of this customer and can push an offer of a discount at a coffee chain right after a piece of jewellery is purchase in a department store. 

Unlike mass-market campaigns that send emails or texts to customers about products that they are either not interested in or do not want at that exact time, Big Data analytics can instantly analyse historical data, with context, to offer real-time benefits to customers. 

With The Economist proclaiming in a 2017 article “the world’s most valuable resource is no longer oil, but data”, there is a clear need for financial firms to embrace the benefits of Big Data going forward.  

As the global market for Big Data analytics in banking is forecast to grow by an annual rate of more than 22% until 2026, banks are increasingly aware of the importance of partnering with established market players to embed Big Data tools in areas of their business where the impact will be felt most significantly. Big Data tools are continuing to change the landscape within the industry and help address key customer pain points, increase retention rates and unlock critical insights about customer behaviour.