The Role of Big Data in the Fintech Revolution

If you’ve taken even a passing interest in the world of fintech over the last decade or so, you cannot help but have noticed that data is the key to almost any and all developments. It is something of a cliché to say so, but data is indeed the gold of the 21st century - the raw material from which nations are built.

You could argue that it’s more accurate to say that data is the oil of the 21st century, though. Much like oil, data must be refined (analysed, in this analogy) to break it down into its useful products. On its own, a vast quantity of data is as useless as crude oil. But fintech has proved to be particularly adept at using data from a surprising array of sources. Let’s take a look at what has already been achieved and what is still to come.

What is “Big Data”?

We have heard of entities like “Big Pharma” and “Big Oil”, used to mean the giants that dominate those particular fields, so it might surprise you to learn that “Big Data” is not the same thing. There is, at time of writing, no monopoly formed by a small number of massive organisations that effectively control all data. There are, however, the Big Tech firms of Google, Facebook, Amazon, Microsoft and Apple, all of whom have enough customers and data sources to be able to make that step pretty easily.

Until that day, Big Data is just what it sounds like - large volumes of data. In fintech terms, this could be customer profiles, transaction records, website and app traffic and so on. With larger firms, the sheer quantity of this data makes it much harder to process, meaning that useful conclusions are nearly impossible to form using traditional methods of data handling.

With the proper handling, Big Data has some significant advantages to offer. You could identify buyer habits - from the broader customer segment perspective right down to individual customers. You also could spot consistent errors, failures and inefficiencies in seconds. You could even spot errors that have been intentionally triggered, such as people trying to defraud your system. It just needs the right handling.

The growth of the Internet of Things (IoT) has made the situation even more complex, but also more interesting. You can find a full explanation of the IoT and its applications in the financial world here.

Big Data as a key enabler

When it comes to developing and refining fintech solutions, having plenty of customer data to work with makes the task significantly easier. Big Data sources and data science departments are better able to provide that since they can provide volume, velocity and variety - that is, they can provide a lot of different data quickly. This is one of plenty of reasons why cooperation between small fintech start-ups and big traditional banks has been so widespread.

On its own, a small company simply cannot generate enough data to be able to test their system effectively. You would need to build up a customer base with a compelling product, but you need the data to make the product - it’s a Catch 22 that Big Data can help to resolve.

The products of Big Data

With access to this scale of data, emerging fintech solutions can predict customer behaviour, increasing revenue by exploiting the sales opportunities it suggests. They can also help make savings by using the information to run sophisticated risk evaluations, allowing banks to make more informed decisions about when to lend money out and to whom.

Perhaps one of the most significant advantages that Big Data has brought to the world of fintech is the ability to respond in real time. Previously, changes in markets or fraudulent transactions might not be detected until it is already way too late to do anything about it. However, when vast amounts of data is being analysed virtually instantaneously, it’s possible to immediately spot warning signs and act accordingly.

It is the embracing of machine learning and AI solutions to Big Data challenges that has allowed the rapid success of smaller fintech firms, leading to the revolution alluded to in the title of this piece. Particularly during the COVID-19 pandemic, it was found that Neo and Challenger banks were easily out-pacing their traditional competitors, in no small part due to the former’s use of Big Data to provide their customers with whatever they need whenever they want it.

The benefits of Big Data

What kind of opportunities can Big Data provide that could possibly make smaller fintech firms preferable to centuries-old institutions? Well, by using machine learning to study the vast amounts of input, processes and output, there are four basic benefits:

1. Customer focus: By learning the customer’s habits, companies can tailor their products to the needs of an actual individual rather than making stock solutions that work for the majority of cases but not all of them.
2. Better security: Fraud can be spotted quicker and stopped more effectively, giving customers far more convenience and comfort with trusting the company with their money.
3. Better risk assessment: A lot of finance is effectively a gamble of one sort or another. It could be trusting a customer to pay back their loan on schedule or it could be investing in a business venture with the ever-present risk that they will fail. Studying Big Data is the same as studying a racehorse’s form, only with far more data points to work from and much less speculation involved. In essence, you remove as much of the gambling as you can.
4. Better customer service: Closely linked with our point about customer focus, but more particularly focused on helping the customer with their problems before they become bigger problems. This can include using chatbots as the first layer of the customer service process. By using large amounts of data to train the bot, it could be set up to solve most problems before needing to involve a human support assistant.

The future of Big Data in fintech

We’ve said, in several articles on this blog, that fintech is undoubtedly the future of finance. Shifting customer expectations and the runaway success of fintech enterprises make that very clear. However, it is important to recognise the role that Big Data has played in that success and in forming those expectations.

The challenge comes when concerns around privacy interact with the desire for a more personalised, less anonymous financial experience. You will have noticed an increase in the amount of websites explicitly asking you to accept their cookies or apps asking you to allow them to track your activity on other apps and sites. Both of these are important data sources for helping to forecast customer behaviour, but a degree of paranoia around how much data random companies are able to gather about you has people concerned about their privacy.

So long as data is managed properly, there is really very little threat to the privacy of most people. The overwhelming majority of data collected does not include personally identifiable information (PII) and merely provides examples of how people from your particular customer segment profile behave. However, finance is one area where PII data is pretty important, especially when it comes to tailoring experiences to specific customers.

That being the case, a major factor in the future of fintech and Big Data will be the development and proliferation of regulations that govern data handling. If this is handled well, fintech firms will be able to continue developing smoothly while keeping invasions of privacy to a minimum. If handled poorly, we could see inconsistencies with how companies handle sensitive data and, potentially, stagnation in the fintech world.