Why Big Data and analytics aren't the answer for banks

It’s a financial world: Why Big Data and analytics aren’t the answer for banks

Why Big Data and analytics aren’t the answer for banks

‘Big
Data’ and ‘analytics’ are amongst the most over-used and abused terms currently
in the business world. They are often sold as the panacea to all known problems
by snake oil sellers across the globe. Banks should focus on true insights and
consequential actions to differentiate themselves and take an industrial view
to data and analytics.

Big
Data and analytics have generated lots of revenue for hardware suppliers, software
providers and consultants. They have also created lots of jobs for people with
skills ranging from basic statistics to advanced mathematical modelling skills.
What is highly questionable is whether all this expenditure has generated value
for the banks that have invested in them?

Like
many new business philosophies and technologies the approach banks have taken
to adopting them is to build them in-house. Just like when computers first
emerged and individual departments took it on themselves to buy their own
computer, hire their own programmers and write their own code to address their
department’s specific requirements (as an aside, It is one of the reasons why
so many banks still today have such dysfunctional IT departments and systems),
banks don’t appear to have learnt the lessons from the past and are adopting
the same approach when it comes to data and analytics. Functions such as risk,
mortgage underwriting, card product management, marketing, finance and treasury
are creating their own local data marts, hiring their own data scientists and
modellers and buying their own query and advanced analytics tools. They are
building models, sometimes in inappropriate tools, with inadequate testing that
the bank’s executives are making critical decisions based on the output from
them.

The
fact that individual departments are doing their own thing is very cost
inefficient is the least of the problems with this approach. Even for banks
that have elected to go for a Centre of Excellence operating model for data and
analytics whereby a central pool of data and analytics experts provide services
to whole bank there is a fundamental problem with this way of addressing data
and analytics.

Building
models in-house is predicated on the basis that every bank is so unique that
the models will provide differentiation from the competition. However banking,
and particularly retail banking, is based around standardised products with
standardised ways of underwriting those products, standardised ways of funding
the products and very largely standardised way of moving the customer’s money. Therefore
spending large amounts of money hiring expensive data scientists and modellers
and then lots of time building models when there are standard models available
to either buy or pay for the use of from the likes of Experian, SAS and other
data and analytics specialty firms makes no sense. Not least of all because
true data scientists need to be continually fed interesting and challenging
problems to crack (something few banks will be able to consistently provide
enough of while specialty firms will be able to) otherwise they get bored and
stressed – a bit like caged lions that are fed raw meat rather than having the
excitement of the hunt.

Unfortunately
the peddlers of Big Data and analytics solutions don’t point out to their
customers and the IT users who buy their solutions don’t acknowledge the
critical fact that:

Data and analytics
without context and insight is of no value to a bank.

Insight
is an unfortunate word because many banks take it to mean having a better
understanding of what is going on inside their banks. However that is only the
half of it. As critical is to have an understanding and the context of what is
going on in the environment that the bank is operating within. What are the
competitors doing, what is happening and could happen in the macro economic
environment and how would that impact the bank’s customers are just some of the
potential questions that need to be answered to create insight. If there had
been a better understanding of some of these questions then it is possible that
the financial crisis of 2008 could have been avoided.

However
insight of its own is not enough. A number of banks across the globe could
rightly claim that they have teams of data scientists who like the PreCogs in
the Tom Cruise film ‘Minority Report’, who were able to predict crimes before
they were carried out, know so much about their customers that they can predict
what they will do next. However having that knowledge but not having a means of
sharing it in a simple and usable way with the banks’ systems and the people
who use those systems means that it is of no value at all.

Insight with the
ability to know the ‘Next Best Action’ and execute on it is what will define
the banks that will emerge as the winners.

This
ability to apply data and insights to bring about great outcomes should not be
limited to use with customers but should also be applied to other areas of the
bank such as pricing models to allow personalised offers, to fraud detection,
to identify money laundering activities and to make better funding decisions.
The list of areas where this could be applied to banks is almost limitless.

However
what it requires is a very different approach to data and analytics then is
largely adopted today. It needs to be driven down by the desired business
outcome with the data required being seen as the very last thing. It needs to
be driven by the business executives not from IT or worst still technology
vendors. It needs to be driven as an industrial process rather than as a
cottage industry. Banks need to understand that where they will be able to
differentiate themselves from their competitors is on their insights and how
well they execute on those. For the rest they should look for best in class
products and services for data and analytics from organisations that are truly
expert in those areas.

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