How Big Data and AI Are Changing the Financial Industry

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The financial industry has never been short of data, but until recently, the bulk of it has been too complex to do anything meaningful with. A little something called AI is gradually changing that. But what impact will that have on the financial industry, and which companies should we be watching?

How Big Data is shaping the world

Big Data is a term used to describe data sets that are too complex for humans to manage quickly and efficiently. According to IBM, it should have one of the following characteristics:

  • High volume
  • High velocity 
  • High variety

For instance, Internet of Things (IoT) devices have really taken hold over the last few years, and they collect data in ways we’ve never seen before. Devices in our homes can continuously and instantaneously take information about the music we’re listening to, the number of steps we’re walking, what we’re searching for on Google — and who knows what else.

Even the world’s best data scientists would struggle to make any sense of that quickly enough without the right tools, and in some cases, the data wouldn’t be usable at all. For instance, what could an individual researcher do with data about someone’s heart rate as they sleep each night? But someone (or rather, something) else can — artificial intelligence.

The role of AI

Artificial intelligence can do what humans can’t: Process, organize, and store Big Data efficiently. It can use techniques such as:

  • Machine learning: Developing algorithms and models that allows software to continuously learn from data, with various applications.
  • Text analytics: Processing large volumes of text to find meaning using machine learning and other statistical techniques combined with linguistics.
  • Predictive analytics: A variety of statistical techniques combined together to make future predictions.
  • Data mining: Extracting large data sets to find patterns and relationships, often to make policy or business decisions.
  • Natural language processing: Programming software to make sense of human speech and written language.

AI tends to get more attention than Big Data, but the two must work together for maximum effectiveness. Artificial intelligence can produce better, more accurate results when it has more data at its disposal — it allows for more context and better predictions instead of sweeping assumptions. 

Big Data, AI, and finance

So, how does any of this relate to the financial industry? Considering UBS Evidence Lab found that three-quarters of banks managing assets worth $100 billion or more use AI in some way, there’s a clear trend.

Let’s look at some of the biggest applications and how they link with Big Data.

Reducing fraud

If banks can monitor transactions and use predictive analytics through AI, they can spot fraudulent patterns in real-time and reduce fraud. For instance, they can compare a customer’s usual patterns against something abnormal.

This is one of the areas that costs banks the most money and which customers find most distressing, so the use of AI here would have a huge impact.

Tailoring to customers

The combination of Big Data and AI makes it easier than ever to understand your customers — and tailor what you’re doing to them specifically. By tracking data, you can understand their motivations, allowing you to market to them and provide products they’d like more.

For instance, it can monitor their transactions and current financial standing to see which products they’d be eligible for, and figure out based on their current purchases what customers like them would be most likely to want. An overdraft or a savings account?

While humans may have been able to do something in the past, the cost and time it would take simply wouldn’t justify the potential returns since the same process would need to be carried out for each individual. With AI, financial companies can develop an algorithm once and use it for every customer.

For instance, the Royal Bank of Scotland of NatWest (NWG) is using AI to improve the homebuying process for its customers, bringing together data on a single platform where AI gives them tailored information.

Back office

The back office might not be the most glamorous side of financial companies, but it’s where a huge amount of resources go. AI and Big Data can be applied to a company’s staff just as they can customers. For instance, AI can analyze what a typical process looks like when a staff member is managing claims or similar, and figure out when they may have made an error.

It could even help to figure out who could be a good hire by finding patterns in current staff members, perhaps compared to psychometric tests or resumes of others.

ING (ING) is using a hybrid cloud environment to make it possible for its staff to access relevant data from anywhere and use it as part of decision-making, all while complying with relevant governance and compliance in different areas.

Developments outside of finance

The financial industry isn’t the only sector set to change dramatically at the hands of AI. The IBM Institute for Business Value found that 84% of organizations intended to increase their use of AI at the start of the pandemic.

Some of the applications outlined above, like back office operations and customer experience, can apply to just about any business.

This means that the companies leading the way for creating AI and Big Data applications stand to grow enormously. Current leaders include IBM (IBM) for its IBM Watson service, Amazon (AMZN) for its Amazon Web Services and Google (GOOG) for its Cloud platform.

The next step for fintech

Big Data and AI aren’t just set to revolutionize the financial industry — they’re set to practically carve out a whole new niche for themselves. We’re only scratching the surface right now, but the big players creating the relevant technology are well established. Why not get involved early?

The views and opinions expressed herein are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

David Cotriss

David Cotriss is an award-winning writer of over 500 news and feature articles on business and technology. His LinkedIn profile can be found at

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