How Is Big Data, Artificial Intelligence And Technology Disrupting The Financial Sector?
2 July 2021
Financial services were one of the first sectors to understand the promise of the Big Data revolution, and the wave of new technology which has come with it – including artificial intelligence (AI).
This isn’t surprising – businesses in the sector traditionally define themselves by their ability to interpret and analyse structured data, and use it for making predictions and decisions.
The shift towards Big Data has meant applying what they know about working with structured data – data which fits neatly into the rows and columns of a spreadsheet – to working with the messy, unstructured data that we are generating today, due to the increasingly digital, connected and online world we live in.
Always keen to develop and exploit a new competitive edge, in recent years the financial sector has put the latest technology to work driving operational changes, increasing rates of fraud detection, improving customer services and developing new products.
However, that’s not to say it doesn’t bring problems of its own!
How technology is causing headaches – and curing them.
The wave of technological change that has swept across the financial sector, and society at large, since the arrival of the internet, has changed things forever – and not just in good ways.
While online banking and technology-driven disruption have brought about improvements in accessibility and customer service, hacking and cybercrime have become common problems. Combating these issues requires enormous amounts of resources – and incurs costs that are inevitably passed on to consumers.
Thankfully, where technology brings new problems, it also offers new solutions. The data we generate and put online offers opportunities for hackers and fraudsters to break into accounts and access information, but it can also be used to react to and combat these threats.
Leading banks, including HSBC, use algorithms to scan the mountains of data that it generates through its transaction logging systems, far more quickly than human analysts would ever be capable of doing.
By tracking patterns of activities and looking for “outliers” – unusual data points such as accounts being accessed at strange times, or from unusual locations – machines can predict how likely a transaction is to be genuine.
And as these machines “learn” from the predictions they make – sometimes hundreds of thousands of learning opportunities per second – they can become increasingly accurate at doing this. Which brings us onto the real “hot potato” in financial technology … artificial intelligence.
Artificial intelligence is changing everything
Big Data is foundational to the new generation of smart, self-teaching machines that are set to drive a seismic shift across every aspect of society, including banking and finance.
The vast increase in the amount of data being generated thanks to the internet and sensor-laden tech such as smartphones and cameras is the “fuel” of AI. Machines consume data voraciously, learning from it faster and more accurately than human brains could ever hope to.
The ideas behind AI aren’t new – “thinking” machines have been theorised for decades. What’s different now has come about due to a set of “perfect storm” circumstances – the critical mass of data being generated thanks to the internet, improved computing technology and the development of leading AI approaches such as deep learning.
Deep learning is a form of machine learning that interprets data through mechanisms known as artificial neural networks – essentially computer algorithms built to mimic the data sorting and decision-making functions of the human brain.
Because the neural networks used in deep learning are highly complex and formed of multiple decision-making layers, they are known as “deep” neural nets. They are still a long, long way from being as complex as an actual human brain – however, they can operate far more quickly and are far less prone to unexplainable errors.
AI has implications for every aspect of business in the financial sector, from fraud detection, as mentioned above, to customer service and operations management.
AI has tremendous implications across the sector and brings its own problems – perhaps the biggest being the ethical implications and the effect it will have on human jobs. Leaders in the banking industry have predicted that eventually, half of their sector’s human workforce could be replaced by machines.
Big Data Fintech Startups/ disruptors
Another challenge facing the financial services industry is the wave of disruptive start-ups that have emerged, and keep emerging. These newcomers are often carving themselves a slice of the customer base by leveraging data-driven technology in an agile way. Customers take a chance when moving their business away from traditional service providers, gambling that a less established innovator will raise the bar when it comes to customer service, convenience or value.
These disruptors include banks that operate primarily through smartphone apps and websites rather than high street branches – reducing overheads, which means they can pass on savings to the customer through lower fees. Adopting data-driven business models means more efficient decisions can be made when it comes to offering loans and investments.
For the customer, it means services such as verifying transactions are not fraudulent, reviewing recent transactions, making instant purchases and transferring money to friends or family are at their fingertips, 24 hours a day.
The growth in popularity of these services means that last year, 38% of personal loans were made by businesses classified as “fintech start-ups” rather than traditional banks and lender.
How Big Data enables superior customer service
Having all this data flying around makes it easier for banks and other financial services organisations to work out what we want, and offer us products and services which accurately match our needs.
Traditionally, opening a bank account or taking out a loan means accepting that you will be subjected to a barrage of marketing aimed at encouraging you to sign up for every service or product under the sun.
Today, banks, including Citibank, use data acquired from customers at every interaction to predict products and services that are likely to be truly useful, at the right time. As well as cutting down on wasted marketing expenditure, making offers a customer is never going to accept, this strategy increases customer satisfaction by reducing the amount of advertising they are inundated with. It can even have an environmental impact, as bank statements won’t be posted out in envelopes stuffed with irrelevant promotional flyers.
Modern banking apps also put data-driven technology at the fingertips of customers themselves. For example, Metro Bank, a relative newcomer to the UK high street, which uses a strategic approach to technology to differentiate itself from traditional competitors, offers a tool called Insights. It uses machine learning to analyse customer spending patterns and make predictions about whether you’re likely to exceed your credit limit before your next paycheck lands, or that unexpected expenses may be about to push you into the red. It also alerts customers to accidental overcharges or double-charges on their account.
Innovations like those mentioned in this article are just the tip of the iceberg. Spending on data technology, particularly applications that can be classified as AI, is only going to increase in the years to come.
Traditional financial services companies, which include banks, investment managers, insurers and brokerages, are finding their markets encroached upon from two directions – from the tech giants above, with their own takes on payment services and money transfer mechanisms, and small, nimble fintech start-ups from below.
Technology is quickly changing the financial services landscape, perhaps more so than any other business sectors outside of retail and marketing. If traditional market leaders want to remain on top, they will have to continue to invest in new technology initiatives to understand and predict customer behaviour, as well as drive operational changes.
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