Digital Banking

Data in Banking: Why is Data important in Banking?

Data in Banking

We all know banks deal with a vast amount of data inside their organizations, but their ability to interpret and extract value from it is the area where they have struggled. In this article, we shall unveil the importance of data in banking.

Role of Data in banking

Data in banking provides an inbuilt advantage to banks over their smaller competitors. They should know how they can improve the customer experience by exploiting the data they hold. It implies learning how to interpret data that allows banks to communicate and send messages to customers at the correct time.

They should address customer segmentation by the use of fast data, which will enable banks to better understand their customers and the context in which they consume services.

 The role of data should ultimately help banks bring value by targeting appropriate products like mortgages, loans, insurance policies at competitive prices. This could be done in real-time and will help banks provide a more personalized and more remarkable customer experience. 

The emergence of cognitive banking

A personalized form of banking has emerged through enhanced data analysis. The ability to extract more value from the data via learning, understanding, and analysis is leading to cognitive banking wherein machine learning, along with artificial intelligence, is transforming the customer experience.

Cognitive banking involves fast data and having good customer experience and interfaces. It also includes automatic or robotic AI deployed in executing data quickly and accurately, thereby improving the banking process.

Banks should reach conclusions on the engagement strategy with their customers either directly or indirectly on real-time data from multiple source. This forms part of the cognitive banking process that encompasses the analysis, processing, and production of insights resulting in new products and deeper targeted customer value.

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How does data quality bolster success in a banking environment?

The banking industry is at present in flux. Traditional banks require contending with new competitors unburdened by legacy technology or business models, making them agile and responsive.

In order to remain competitive traditional banks require making intelligent decisions towards how to serve their customers, and to do so, quality data is required.

Banks are looking at new ways to do business, and it requires a deep understanding of their market and customers. Apart from it the banking industry should also comply with many regulations. These challenges require data but having data is not enough. They must have quality data that can be trusted.

The quality of data is also essential for compliance reasons. For the purpose of Anti-money laundering, you require to verify information, trace transactions, etc. and all this requires accurate and accessible information. 

Better quality data frees up capital to provide better returns to shareholders. The regulations around the world require customer’s data to be of adequate quality so that decisions made based on data are not prejudicial.

Moving on from Big Data to Fast Data

Deriving great value and insight need fast data. The main feature of fast data is the rapid gathering and the analysis of data in real-time. It is the ability to consume, analyze, and execute on the insight from multiple data sources. Unlike big data, which focuses on storage, fast data is consumption-oriented and gives a richer context in terms of analysis and decision making. This allows banks to provide an enhanced and personalized user experience.

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Data in banking: The value of data

Traditional banks face stiff competition from challenger banks and fintech; therefore, they must make sure that they use data effectively. As the digital revolution transforms the banking sector, the value of data has risen to the next level of significance. In the last century, oil was considered to be the most valuable commodity in the world, but today data is taking a similar position.

Banks must become data-driven organizations in order to deliver cognitive banking. It will lead to new financial products and services based on the information that is better suited to the expectations and the needs of customers. The value of data lifts banking to a higher place and beyond just a transactional arrangement to one that supports the customer lifestyle and interests.

Data in banking: Data led organization

Banks can use data to drive value in these ways:Data led organization

  • Data led personalization

The use of data to personalize banking has the potential to improve customer engagement and increases revenue as well.

  • Data led prediction

Banks that are rich in data will enable them to predict the future. The ability to predict the future can help them to improve credit decisions, fraud detection, and forecasting of liquidity needs, thereby reducing cost and mitigating risk.

  • Intelligent insights

It is noteworthy to mention here that intelligent insights from data can boost revenues by enabling the business to make accurate decisions.

  • Intelligent automation

Banks can use data to reduce business process and operational risk. These include augmenting robotic automation with artificial intelligence to make value judgments and using data gathered through automation to solve upstream issues.

  • Digital products and services
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With the use of data in banking, banks can create new products and services. This includes monetizing banking data or suing banking data while interacting with non-banking institutions to develop an ecosystem of services. 


Thus it can be concluded that data in banking is critical. Quality data can help banks on many fronts and help them provide enhanced customer experience to their customers. Moreover, as stated earlier, the value of data lifts banking to a higher place that supports the customer lifestyle and interests.

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