Big data and analytics – implications for the finance industry

Big data and analytics – implications for the finance industry

Big data and analytics are crucial aspects of the modern financial services industry.  New data-driven services can be leveraged to increase revenues; costs can be reduced and efficiencies improved, thus increasing competitiveness; and security can be improved, providing customers with better, safer services. Globally, big data analytics is big business.  It is predicted that its market will exceed $200 billion in 2020, and one of the biggest drivers of this growth is the banking industry.

FinTech companies are challenging the traditional banking sector, and it is big data that is allowing them to do so. They can offer customers fast and low-cost financial services, make and receive online payments, and offer benefits such as peer to peer lending. While the traditional banking sector tends to move much more slowly, there is certainly evidence that they are catching up rapidly. While not as fleet of foot as the new FinTech businesses, they are progressively embracing the benefits big data can offer particularly in terms of improving customer services and customer satisfaction. IBM reports that 71% of banking and financial markets businesses report that big data analytics is creating a competitive market for their businesses, almost double the number in 2010.

The problem is not so much the data. The finance sector generates and stores it in abundance. The real challenge is analytics. According to IBM, 90% of the data collected during 2020 will be “dark data”; in other words, data that is stored and never used. Organisations collect it for a myriad of reasons such as regulatory compliance, or simply because they think that it might come in useful sometime in the future. However, it rarely does. Most data has a limited usefulness span; it is out of date before anyone gets around to putting it to good use. While storing dark data is relatively inexpensive, it is ultimately dangerous, especially in the finance industry where it retains personally identifiable data.

Why does the finance industry need big data?

Big data analytics apply sophisticated analytic techniques to big, diverse data sets of structured, semi-structured and unstructured data, usually from multiple sources. While the term big data has been used for several decades, today it is usually taken to mean data sets too large for conventional database applications to handle in real-time.

In a nutshell, by collecting and analysing huge quantities of data, organisations can anticipate the behaviour of their customers and develop strategies to provide better, safer, and more relevant services. The value of the data lies almost entirely in the way it is gathered, analysed and interpreted. By itself, big data has no value.

How the finance industry uses big data

The top five applications of big data in the finance industry are in order:

  1. Customer analytics – employed by 55% of the sector
  2. Risk and financial management – employed by 23% of the sector
  3. New business models – employed by 15% of the sector
  4. Operational optimisation – employed by 4% of the sector
  5. Employee collaboration – employed by 2% of the sector


Customer centred analytics is now the top priority. This is a radical departure from the past when the financial sector was mainly product centred. Data insights, systems and operations now focus mainly on the customer. By learning how to anticipate changing markets and customer preferences, banks and other financial-services companies can quickly develop new customer-centred products and services to seize new market opportunities and retain customer loyalty.

Some examples of this are:

  • Offering customers credit cards with customised interest rates and incentives based on their historical spending patterns
  • Suggesting products that offer value and are appropriate to customer spending patterns.
  • Analysis of customers ATM usage and interactions with call centres to increase customer engagement
  • Speech analytics to determine main reasons for repeat calls to call centres from customers
  • Predicting when a customer might close their account and taking steps to dissuade them from doing so

Big data is being used by a large segment of the finance sector (23%) to enhance risk and financial management, for instance by optimising return on equity, fighting fraud, reducing operational risk, and meeting regulatory and compliance requirements.

Understanding risk is a crucial aspect of a large part of the finance industry. For instance, insurance companies must analyse risk when insuring a customer; investment businesses must analyse market risks and tailor their products to the amount of risk a customer is willing to accept. Big data analytics is improving these processes by providing faster and more reliable solutions.

Fraud prevention is a huge factor. Fraudsters are becoming ever more sophisticated and often, especially in the realm of state-sponsored fraud, have massive resources available to develop new ways of committing cybercrime. Big data analytics empowered by artificial intelligence and machine learning is a major weapon in combatting these threats and identifying cyberattacks and fraudulent transactions.

The future of big data in finance

The finance industry is no stranger to data. The sector has always been data-driven, and to an extent, big data is more of the same, except that now we generate so much of it that we are unsure of how to exploit what appears to be, and to a large extent is a highly valuable resource. The sector is using it to improve the experience of its customers, to develop new, better and more convenient products, to mitigate risk and combat fraud, and to make better investment decisions.

Crucial to putting big data to good use are good analytic tools. While many of the tools used to analyse big data rely on conventional computer algorithms, machine learning and artificial intelligence is making an increasing impact. Already this is being used by the FinTech sector to disrupt systems such as conventional credit scoring and providing financial products and credit to customers who previously would not qualify for them.

Big data and machine learning will continue to change the financial industry, almost certainly for the mutual benefit of both the customer and the business.

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