Banking
WHY DATA HAS BECOME BANKS MOST IMPORTANT COMMODITY
Published by Gbaf News
Posted on July 28, 2017
8 min readLast updated: January 21, 2026
Global Banking and Finance Review
Company
Published by Gbaf News
Posted on July 28, 2017
8 min readLast updated: January 21, 2026
Christian Ball, Head of Retail – Atlantic Region
Introduction
It is commonly recognised that the digital revolution sweeping the financial sector is a serious threat to traditional banks. The pace of change that has occurred over the last decade has left many institutions feeling uncertain on how best to embrace the digital future. Faced with new competitors and advances in technology, it would be easy to assume that traditional banks will inevitably be left behind as customers turn to challenger banks and third party providers for financial services and products. What is often overlooked is that as incumbents, large banks have certain advantages that their smaller rivals do not possess. Arguably the biggest advantage is that of data.
Banks have always held vast amounts of data inside their organisations but their ability to interpret and extract value from this commodity is something they have historically struggled to achieve. With new regulations to be introduced and more competitors in the marketplace, this situation is set to change.The Competition and Markets Authority (CMA) and the European Union directive PSD2 are at the forefront of this change that is driving banks towards implementing an Open Banking strategy.
Open Banking will make it easier for customer data to be shared between banks, customers and other third party providers – transforming the relationship between banks and customers. This access to data gives challenger banks and fintechs the opportunity to develop new innovative financial products and services but traditional banks have an ideal chance to improve their customer experience through the vast amounts of data they hold.
How to create value for customers through data becomes an important factor in the digital economy. It means establishing a more accurate understanding of customers and the context in which they consume services. Communicating with customers about products and services in an appropriate and timely manner and ultimately creating a more enhanced customer experience through the value of data.
The emergence of Cognitive Banking
A more personalised form of banking is beginning to emerge through enhanced data analysis. This ability to extract more value from data through learning, understanding and analysis is leading towards cognitive banking; where artificial intelligence and machine learning are changing the customer experience.
Cognitive banking is about having fast data and good User Experience and Interfaces (UX & UI) for customers. It also includes automatic or robotic artificial intelligence deployed in executing data quickly, accurately, cost effectively and predictably, thus significantly improving the digital banking process. In the world of digitalisation and the digital economy, cognitive banking expresses all the digital requirements and more.
As well as providing services in this way, banks must reach conclusions on the engagement strategy with their customer base, either directly or indirectly on real time data from multiple sources. This forms part of the cognitive banking process which encompasses the analysis, processing and production of insights resulting in new products and deeper more targeted customer value.
Banks and financial institutions are working towards establishing new business opportunities by identifying how customers consume and are made aware of products. Decisions can be made on which additional services can be offered to secure customer loyalty, along with understanding how banks should communicate specifically with individuals through applying more behavioural analytics to segmentation.
If banks apply greater machine learning and artificial intelligence techniques and technology, a far more personalised and focused message is created, one that is targeted more effectively and with the added benefit of improving each customer’s appreciation of what their bank can do for them.
Moving from Big Data to Fast Data
Deriving greater value and insight requires ‘Fast Data’. The defining feature of Fast Data is the rapid gathering and analysis of data in real time. It is about the ability to consume, analyse and execute on the insight generated from multiple data sources. Unlike big data, which focuses on storage. Fast data is a consumption orientated view and provides a richer context in terms of analysis and decision making. This allows banks to provide a more enhanced and personalised customer experience.
The value of data
Faced with increased competition, traditional banks must utilise data effectively and transform themselves into data-driven organisations that will deliver knowledge banking. This will lead to better financial products and services for customers that suit their needs and expectations.
Banks can however go one step further by extrapolating valuable data from their large established customer bases and segments – something challengers do not have. This data can be monetised by allowing third parties to access segmentation information about their own customer base, detailing consumer behaviour. This permissive data which customers have agreed to share is an important development. This opportunity to sell insights based on segmentation knowledge offers the possibility of a profitable new revenue stream for banks.
Christian Ball, Head of Retail – Atlantic Region
Introduction
It is commonly recognised that the digital revolution sweeping the financial sector is a serious threat to traditional banks. The pace of change that has occurred over the last decade has left many institutions feeling uncertain on how best to embrace the digital future. Faced with new competitors and advances in technology, it would be easy to assume that traditional banks will inevitably be left behind as customers turn to challenger banks and third party providers for financial services and products. What is often overlooked is that as incumbents, large banks have certain advantages that their smaller rivals do not possess. Arguably the biggest advantage is that of data.
Banks have always held vast amounts of data inside their organisations but their ability to interpret and extract value from this commodity is something they have historically struggled to achieve. With new regulations to be introduced and more competitors in the marketplace, this situation is set to change.The Competition and Markets Authority (CMA) and the European Union directive PSD2 are at the forefront of this change that is driving banks towards implementing an Open Banking strategy.
Open Banking will make it easier for customer data to be shared between banks, customers and other third party providers – transforming the relationship between banks and customers. This access to data gives challenger banks and fintechs the opportunity to develop new innovative financial products and services but traditional banks have an ideal chance to improve their customer experience through the vast amounts of data they hold.
How to create value for customers through data becomes an important factor in the digital economy. It means establishing a more accurate understanding of customers and the context in which they consume services. Communicating with customers about products and services in an appropriate and timely manner and ultimately creating a more enhanced customer experience through the value of data.
The emergence of Cognitive Banking
A more personalised form of banking is beginning to emerge through enhanced data analysis. This ability to extract more value from data through learning, understanding and analysis is leading towards cognitive banking; where artificial intelligence and machine learning are changing the customer experience.
Cognitive banking is about having fast data and good User Experience and Interfaces (UX & UI) for customers. It also includes automatic or robotic artificial intelligence deployed in executing data quickly, accurately, cost effectively and predictably, thus significantly improving the digital banking process. In the world of digitalisation and the digital economy, cognitive banking expresses all the digital requirements and more.
As well as providing services in this way, banks must reach conclusions on the engagement strategy with their customer base, either directly or indirectly on real time data from multiple sources. This forms part of the cognitive banking process which encompasses the analysis, processing and production of insights resulting in new products and deeper more targeted customer value.
Banks and financial institutions are working towards establishing new business opportunities by identifying how customers consume and are made aware of products. Decisions can be made on which additional services can be offered to secure customer loyalty, along with understanding how banks should communicate specifically with individuals through applying more behavioural analytics to segmentation.
If banks apply greater machine learning and artificial intelligence techniques and technology, a far more personalised and focused message is created, one that is targeted more effectively and with the added benefit of improving each customer’s appreciation of what their bank can do for them.
Moving from Big Data to Fast Data
Deriving greater value and insight requires ‘Fast Data’. The defining feature of Fast Data is the rapid gathering and analysis of data in real time. It is about the ability to consume, analyse and execute on the insight generated from multiple data sources. Unlike big data, which focuses on storage. Fast data is a consumption orientated view and provides a richer context in terms of analysis and decision making. This allows banks to provide a more enhanced and personalised customer experience.
The value of data
Faced with increased competition, traditional banks must utilise data effectively and transform themselves into data-driven organisations that will deliver knowledge banking. This will lead to better financial products and services for customers that suit their needs and expectations.
Banks can however go one step further by extrapolating valuable data from their large established customer bases and segments – something challengers do not have. This data can be monetised by allowing third parties to access segmentation information about their own customer base, detailing consumer behaviour. This permissive data which customers have agreed to share is an important development. This opportunity to sell insights based on segmentation knowledge offers the possibility of a profitable new revenue stream for banks.
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