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How big data is transforming banking

Big data is a central element of the digital transformation process that is happening at a rapid rate in the banking and financial sectors as well as numerous other industries around the globe. The amount of data involved is enormous, with estimates that banks and related financial services industry are sitting on at least one exabyte (1 billion gigabytes) of stored data – and more is being generated every second. Not only is the volume of data staggering, but the value to the industry is equally impressive with the big data analytics market expected to top $USD62 billion by 2025.

This collected data is a mixture of both structured and unstructured. Structured data is in the form of things like credit card numbers, names and addresses and other details captured during transactions. This accounts for about 20% of the data collected and stored by banks with the remaining 80% being unstructured data in the form of emails, recorded calls, social media and more. The value of this data to banks can only be realized when it can be analyzed and turned into actionable insights. The mainframe computing systems utilized by most banks to run their operations are simply not suited to perform these tasks.

One of the greatest technological advancements in the past decade has been the commodification of the enormous computing power and storage required to make it economically viable to use big data in a meaningful way. Banks are now embracing the cloud, with a 2019 study by financial advisory firm Celent revealing that 62% of financial institutions intend on moving the majority of their operations to the cloud by 2024.

Big data in banking

How is big data used in banking today?

Now that banks and financial institutions have the capabilities to capitalize on big data, what are some of the ways it is being used? When combined with advanced data visualization tools, artificial intelligence and machine learning algorithms banks are able to:

Realize more advanced security and fraud protection than ever before

Credit card fraud alone is estimated to cost US banks USD$11B in 2020, so combating fraud is a top priority for financial institutions. With the ability to work with transaction data across multiple channels in real-time, banks are able to catch fraudulent transactions faster than ever before. By real-time analysis of a customer’s transaction data across multiple product lines such as checking, mortgage and credit card accounts, banks are able to build an accurate understanding of their individual usage patterns and be able to immediately flag any suspicious uses. These big data driven fraud prevention tools are already making a big difference – in 2020 alone Visa’s A.I. software stopped an estimated USD$25B in fraudulent transactions.

Mine social media data to understand customer sentiment

With so many people communicating with and about companies online banks are seeing the value in social media analytics to better understand customer sentiment and look for new opportunities to improve their product and service offerings. This method of analysis has proven to be more effective for banks than cold-calling customers for feedback.

Customize service offerings to boost customer satisfaction

Big data analytics allows banks the ability to enhance the products and services they already offer with a level of personalization that was not previously available. This data can help them predict services a customer will need at precisely the right time. Travel booking transactions could be used to automatically trigger an increase in spending limits and authorize foreign transactions, all without a customer needing to call. Other major life events such as births and marriages can be detected with automated analysis of transactions, providing a perfect opportunity to offer relevant services. This same data can also allow banks to be empathetic to the needs of their customers, especially during times of crisis, such as the Covid-19 pandemic to proactively reach out and offer relief in the form of waiving penalties for late payments.

Automate more than ever

When combined with artificial intelligence and machine learning algorithms banks are in a position to automate significant portions of their operations. Big data powered automations are being used in ways such as drastically speeding up the time to open new accounts while still complying with strict regulatory requirements. Automated processes can also be used to monitor for the signs of customer dissatisfaction and allow banks to reach customers with the appropriate retention measures before an account is closed.

Challenges banks face

What are some of the challenges banks face when utilizing big data?

Cloud computing allows all competitors, especially fintech startups, access to the same tech infrastructure, which has essentially now become a commodity. In this new technological environment, it is up to banks to differentiate amongst themselves and non-traditional competition by how they utilize the data. However, that is not to say that banks don’t have some challenges to overcome during this process of digital transformation. Two significant obstacles include:

Being treated as a utility by competitors

While some banks have been open to forming strategic partnerships with fintech companies, others are understandably wary of helping companies that would otherwise seek to eat at their profit centers. It is clear that the digital native fintech companies have significant expertise in the realm of extracting valuable insights from big data to create new and valuable business models. However, traditional banks have legitimate concerns that by providing too much access to their customer base and associated data to these potential competitors, they may end up in the role of a utility.

Privacy and regulatory issues

As more and more personal data is collected and stored by banks, there are inevitably going to be concerns about privacy and the security of their information. As more governments around the world enact legislation such as the General Data Protection Act (GDPR) and the California Consumer Privacy Act (CCPA), it will create additional hurdles for banks to maintain compliance with the ways in which they collect, store and process personal data.

Conclusion

With more fintech companies entering the market each year and encroaching on financial products and services previously only offered by traditional financial institutions, it has become critical for banks to invest more in big data initiatives. Traditional banks already have an advantage over newcomers in the finance sector – their enormous established customer bases and wealth of historical data they provide. Combined with the ability to utilize big data analytics to improve the customer experience, reduce operational costs and improve efficiency, banks have the ability to maintain their status as market leaders. If they don’t fully embrace this opportunity, they may well find themselves as stepping stones to a new generation of financial giants.

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