How Big Data, AI Disrupting Finance

Financial Sector?
How Is Big Data
AI And Technology
Disrupting The

© 2019 Bernard Marr, Bernard Marr & Co. All rights reserved
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IntroductionIntroduction
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.
How Is Big Data, Artificial Intelligence And
Technology Disrupting The Financial Sector?

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.

Artificial intelligence is changing everything
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.

How Big Data enables superior customer service
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.
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.
The future?
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.

© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website
© 2017 Bernard Marr , Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a
strategic business & technology advisor to governments and companies. He helps
organisations improve their business performance, use data more intelligently, and
understand the implications of new technologies such as artificial intelligence, big data,
blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is a frequent
contributor to the World Economic Forum and writes a regular column for Forbes. Every day
Bernard actively engages his 1.5 million social media followers and shares content that
reaches millions of readers.
Visit The
Website

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How Big Data, AI Disrupting Finance

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