Big Data in Banking industry — benefits and challenges

Have you ever thought of the amount of data you create every day? Every credit card transaction, every message you send, every images/videos that we upload on social media etc. It all sums up to a 2.5 quintillion bytes of data that the global population produces on a daily basis.

This opens endless opportunities for the most forward-thinking businesses across a number of domains to capitalize on that data, and the banking industry is no exception. In the mid-90s, Bill Gates said that ‘banking is necessary, banks are not’. In a recent PwC survey of more than 1,300 financial industry executives, 88% said they feared their business was at risk to standalone financial technology companies in areas such as payments, money transfers, and personal finance, and 51% said they believe they could lose as much as 40% of their revenue to standalone FinTech firms.

In this blog, we will talk about common use cases for big data in banking.

Role of big data in banking: Benefits and challenges

According to the study by IDC (International Data Corporation), the worldwide revenue for big data and business analytics solutions is expected to reach $260 billion by 2022. This year, the projected numbers will hit $166 billion, up 11.7% compared to 2017.

It comes as no surprise that banking is one of the business domains that makes the highest investment in big data.

Benefits of big data in banking;

1. Big data gives you a full view on your business

2. It allows you to optimize and streamline your internal processes

3. Big data analytics in banking can be used to enhance your cyber security and reduce risks

Traditional banks have is the vast amount of financial data they hold about their millions of customers. They also have the structure and capital to exploit it. Speaking at the Google Cloud Next Conference, CIO at HSBC, explained that, ‘Apart from our $2.4 trillion dollars of assets on our balance sheet, we have at the core of the company a massive asset in our data.

Major challenges in banking;

1. Legacy systems struggle to keep up: The banking sector has always been relatively slow to innovate: 92 of the top 100 world leading banks still rely on IBM mainframes in their operations. No wonder fintech adoption is so high. However, when it comes to big data, things get even worse: most legacy systems can’t cope with the growing workload. Trying to collect, store, and analyze the required amounts of data using an outdated infrastructure can put the stability of your entire system at risk. As a result, organizations face the challenge of growing their processing capacities or completely re-building their systems to take up the challenge.

2. The bigger the data, the higher the risk: It is clear that banking providers need to make sure the user data they accumulate and process remains safe at all times. Yet, only 38% of organizations worldwide are ready to handle the threat, according to ISACA (Information Systems Audit and Control Association) International. That is why cyber security remains one of the most burning issues in banking.

3. Big data is getting too big: With so many different kinds of data and its total volume, it’s no surprise that businesses struggle to cope with it. This becomes even more obvious when trying to separate the valuable data from the useless. Despite the mentioned challenges, the advantages of big data in banking easily justify any risks. The insights it gives you, the resources it frees up, the money it saves — data is a universal fuel that can propel your business to the top.

The question is how to use big data in banking to its full potential.

Data is known to be one of the most valuable assets a business can have. Yet, it’s not the data itself that matters. It’s what you do with it.

Here are some examples of Data Lake (big data) in banking applications:

Personalized customer experience:

Banks use big data to get to know their users and, as a result, find new ways to cater to them, connect in a more meaningful way, and deliver more value. Your data can give you valuable insights into user behaviour and help you optimize your customer experience accordingly. For example, by having a complete customer profile and exhaustive data on product engagement at hand, you can predict and prevent churn. This approach is reportedly used at American Express. The company’s Australian branch relies on sophisticated predictive models to forecast and prevent customer churn.

User segmentation and targeting:

McKinsey finds that using data to make better decisions can save up to 15–20% of your marketing budget. Taking into account that banks spend on average 8% of their overall budgets on marketing, tapping into big data sounds like a great opportunity to not only save, but generate additional revenue through highly targeted marketing strategies. By using big data, you can better understand your customers’ needs, pinpoint problems in your product targeting and find the best way to fix existing problems.

For example, Barclays has been using the so-called “social listening”, i.e. sentiment analysis, to source actionable insights from user activity on social networks.

Business process optimization and automation:

Further research from McKinsey reveals that around 30% of all work in banks can be automated through technology, and the key to this lies in big data. As a result of advanced automation, banks can experience significant cost savings and reduce the risk of failure by eliminating the human factor from some critical processes. JP Morgan Chase & Co. is one of the automation pioneers in the banking services industry. The company currently employs several artificial intelligence and machine learning programs to optimize some of their processes, including algorithmic trading and commercial-loan agreements interpretation.

Improved cyber security and risk management:

On top of optimizing its internal processes, as mentioned above, JP Morgan Chase relies on big data and AI to identify fraud and prevent terrorist activities among its own employees. The bank processes vast amounts of data to identify individual behaviour patterns and reveal potential risks. Another leading financial service provider, Citibank, is also betting big on big data technologies. The company is investing in promising start-ups and is establishing partnerships with tech companies as a part of its initiative called Citi Ventures. Cyber security is one of the major spheres of interest the company has been exploring recently. As a result, Citibank can spot any suspicious transactions, e.g. incorrect or unusual charges, and promptly notify users about them. Apart from being useful for consumers, the service also helps payment providers and retailers monitor all financial activity and identify threats related to their business.

Better employee performance and management:

Big data solutions in banking allow companies to collect, make sense of and share branch (as well as individual employee) performance metrics across departments in real time. This means better visibility into the day-to-day operations and an elevated ability to proactively solve any issues.

A global banking provider, BNP Paribas, collects and analyzes data on its branch productivity to identify and swiftly fix existing problems in real time.

Conclusion

As you can see, there are many examples of how big data is used in banking. The maximum potential of big data in banking is still to be harnessed. Big data in banking sector can help you improve and grow your business.

Sources:

Easternpeak.com

channels.theinnovationenterprise.com

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kimberelywillcox

BBN Times

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