Financial Software

Big Data in Banking: How it Works, Who Benefits and What’s Next





Financial Software


Big Data in Banking: How it Works, Who Benefits and What’s Next

Published: 2023/04/14

7 min read

What is big data, exactly? It seems like everybody is talking about big data these days. It gets mentioned all the time and everywhere – from inspiring TED talks to Forbes’ predictions about the future of business. It has become a thing that almost every company in the world wants to analyze and gain benefits from. The question is — why? To provide an answer, first we need to cover the basics.

The term “big data” refers to large volumes of data sets of structured and unstructured information that grow exponentially with time. These sets are too large or complex to be stored on regular hard drives or analyzed using traditional data processing software. In other words, you need a lot of space and computing power to be able to collect and analyze big data.

So, why would you go to such lengths to get the space and technology to enable you to process big data? Because it can help you, for example, identify trends and patterns in consumer behavior that you would never find on your own. Later on, you can use those findings to solve business problems and use data science solutions. Sounds good? It does to the banking industry. Here’s what we mean by that.

Read more: Data collection in healthcare

Applications of Financial Big Data

Banks and financial institutions process huge amounts of data every second. Every transaction, every newly created account, every transfer, payment, loan and deposit – all of that is data that once was useful only to accountants and auditors but is now a source of knowledge on how bank customers behave. It’s clear banking systems development services are a complex endeavor.

When analyzed properly, data can tell us how often customers spend their money and on what, how often they use their banking services (and which ones in particular), what they are interested in, and even what dreams they have. To put it differently, all of the information that a given bank collects and processes is big data and it can help a bank better understand its customer base, improve customer experience and drive profits.

Also, let’s not forget that big data is the fuel that drives the artificial intelligence(AI)-based data analytics systems of today. With AI, banks can create more personalized services based on past interactions with their consumers.

According to research done by The Economist Intelligence Unit and Temenos, more than 80% of bank and credit union executives believe that value unlocked with the use of AI will be the key differentiator between winning and losing institutions. The value they are talking about is data-driven. This explains why banks and financial institutions are so interested in big data – and we haven’t yet discussed the benefits big data can produce for them in more detail. Let’s focus on that now.

Read also:  What is Data Science as a Service (DSaaS)?

The Benefits of Big Data in Banking

A 360 Customer View

By analyzing big data, banks and credit unions can get to know their customers better and find out what their wants and needs are. Equipped with the right technology, financial institutions can go way beyond traditional profiling tactics and build consumer profiles that are much more detailed and revealing.

This means they don’t need to rely only on demographics, but instead, they can focus on some of the more idiosyncratic characteristics of their customers. Why? To find new ways to develop their relationships with these customers. For that, they can analyze answers to questions, including:

  • Which products do their customers currently have?
  • When do they most often use the bank’s services?
  • How many bank accounts they have?
  • Which offers do not interest them?
  • Which device do they use for making payments?
  • What is their attitude toward the bank and its products?
  • Which features of online banking apps are most appealing?

Having rich consumer profiles that include this kind of information beats the information obtained from surveys and questionnaires. And the best part is – you can finally stop asking clients to take part in anything. You get your answers from their past interactions.

Improved Customer Segmentation

By knowing who your customers really are, you can divide them into groups that share similar interests, goals, needs, and preferences. Age, gender, or location may therefore be only a fraction of what a given segment represents.

A Bank’s customers usually belong to more than one social group, so this base can include, for example, computer game enthusiasts, football fans, action movie aficionados, and martial arts students all at the same time. Creating a segment that targets customers who buy computer games provides more opportunities than creating one based on the factor that each member is 25 years old.

So, being aware of what makes people feel satisfied and happy in their lives can help banks better address their audiences and improve marketing communication significantly — which brings us to the next benefit.

Read also: What is branchless banking?

Enhanced Personalization

By analyzing big data, banks can create the right message for the right customer to reach them at the right time and place. In other words, financial institutions can use big data to tailor the experience to each individual and thus create long-lasting customer relationships based on loyalty.

According to research done by McKinsey, more than 70 percent of customers expect companies to provide personalized interactions. This means that every consumer wants to be acknowledged for who they are because, as Don Draper from Mad Men would say “in the end, noone wants to be one of a hundred in a box.”

If your competitors are using personalization and you don’t, your customers may soon start to feel like you don’t really care about them and will move on and start a new relationship with a different brand. It’s worth noting that banking as a service might also deliver the tailored-made technology to give you a competitive edge.

Not to mention that having data and not using it means missing out on one of the greatest business opportunities of the 21st century. Therefore, personalization is no longer an option in the financial industry — it is a necessity.

Read also: Big Data Consulting

Fraud Prevention

Using modern data analytics to monitor customer spending habits and find patterns in customer behavior can also help today’s banks to identify suspicious anomalies – things that are out of the ordinary and might suggest that someone is trying to commit fraud.

There can be cases where a system detects that a user (who has managed his finances wisely so far) has suddenly spent or withdrawn all their savings from the account. These sorts of situations may indicate fraud. By being aware of a customer’s activities, not only can a bank  quickly check with a customer about out-of-the-ordinary behavior, but it can also try to find the perpetrators and recover the funds. Nothing will increase the confidence of a bank’s customers as much as knowing that someone cares about the safety of their accounts.

In their report on “The State of Fraud and Financial Crime in the U.S.”, Featurespace reveals that more than 60 percent of financial institutions have reported an increase in fraud. According to many observers, the situation is highly likely to get much worse in the upcoming years. Therefore, proactive action in this area, i.e. the use of new technologies to stop fraudsters, is also something that will be expected from all financial institutions.

Read more: Cloud computing in banking 

The Challenges of Big Data in Banking

For banks to say that they want to incorporate big data analytics into their strategy is one thing, but for them to actually do it is a different thing entirely. The benefits are plenty but there are also a few challenges that come with big data. Here’s what banks and financial institutions should be aware of before they get their hands on one of the modern data analytics tools.

Legacy systems are not powerful enough to run data analytics

Most of the systems that banks are using (such as old-fashioned IBM mainframes) are not capable of handling the workload caused by today’s modern analytics tools. This means that if a bank wants to use data analytics, it must create a proper IT environment for it first — with enough computing power and space.

Read also: AI in Banking: Trends, Examples, Benefits and Challenges

Your data has to be fully accurate to produce great business results.

You can’t expect any data analytics tool to give you any valuable clues or reliable information on customer behavior if you put inaccurate, incomplete, or inconsistent data into it. If you do, you will be basing your strategy on incorrect assumptions. To reap the benefits, you must properly manage the data you collect and conduct audits to get rid of entries that are incorrect.

The more data you have, the more difficult it gets to keep it secure

With all the data privacy laws such as GDPR and CCPA, you must make sure that the data you store is kept safe and sound at all times. There is a reason why “big data” starts with “big” — the amount of information needed to learn more about your customers is massive. Therefore, you must have a game plan regarding where you want to keep it. A data center? A cloud vault? Internal servers? You have a few options, but choose wisely — the security of your clients’ data depends on it.

If you want to learn more about elevating your financial software solutions, or discuss your situation, use this contact form – we’re eager to share our expertise, and our experts are happy to help you find the best solutions.

Read also: Omnichannel Banking: Definition, Trends and Benefits 

About the authorSoftware Mind

Software Mind provides companies with autonomous development teams who manage software life cycles from ideation to release and beyond. For over 20 years we’ve been enriching organizations with the talent they need to boost scalability, drive dynamic growth and bring disruptive ideas to life. Our top-notch engineering teams combine ownership with leading technologies, including cloud, AI, data science and embedded software to accelerate digital transformations and boost software delivery. A culture that embraces openness, craves more and acts with respect enables our bold and passionate people to create evolutive solutions that support scale-ups, unicorns and enterprise-level companies around the world. 

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