FEATURE: AI
consider the exception handling process
and automate as much of this as possible,
introducing efficiencies and mitigating
risk simultaneously.
Furthermore, in many instances, banks
require multiple levels of human approval
for transactions to be cleared. This is
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already have in place when it comes to
managing their data and processes. Through
refining and automation of processes, a
significant amount of human activity can
be taken out of the system and this can be
repurposed to give the bank more capacity
to focus on areas such as improved customer
service or the design of new offerings.
GETTING INSIGHTS FROM DATA IS
THE FIRST STEP TO UNDERSTANDING
WHERE EFFICIENCY CAN BE BUILT INTO
THE INTERNAL PROCESSES OF BANKS.
another time-consuming process that
can be transformed through AI. Instead
of having two or three people view each
transaction, the AI process can deliver the
same level of expertise consistently in real
time to improve SLA management and
improve service to customers.
It is not about reinventing the wheel but
optimising the robust processes banks
Modern practices
Using modern applications that can
integrate with existing data and processes,
banks are able to generate insights from
start to finish.
For example, look at the typical ATM
infrastructure that must be managed daily.
Transactions and GL account balances
must be reconciled to ensure machines are
working correctly, that no fraud is taking
place, and there is always the right amount
of cash available for banking customers
without over exposure of capital reserves.
Using people to reconcile and investigate
discrepancies is slow and inefficient. But
using AI toolsets mean these tasks can be
managed consistently, at high speed and
with full auditability. Volume or capacity
constraints are then no longer an issue.
This extends into customer service as well,
improving the customer experience when
queries or complaints arise as there can be
immediate action taken rather than waiting
for a human to perform analysis and then
take a decision.
An AI layer can be implemented to sit on
top of existing processes while integrating
into back-end legacy systems to deliver
the value banks require. Banks have high
quality data, but it is not always accessible.
Using AI to help manage the high data
volumes can bring about significant
improvements in operational efficiency
which will ultimately deliver a better
customer experience. n
Ensuring the
success of AI in retail
Forward-thinking retailers are using Artificial Intelligence (AI) to ensure they’re
sitting at the top of the tree in an often turbulent and unpredictable sector. And
as Mylo Portas, Head of Retail, Peak, tells us, those who are taking the market by
storm are using the technology to find new ways of driving sales and improving
the experience of their increasingly expectant customers.
A
I can give retailers end-to-end
visibility of their whole business,
increasing sales and conversions,
improving the lifetime value of customers
and improving efficiencies across the entire
business – not just in siloed departments.
In fact, it’s predicted that 325,000 retailers
will be using Machine Learning in some form
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INTELLIGENTCIO
by 2023 – the benefits of AI to retailers are
undeniable, so why isn’t everyone in retail
taking advantage of it? The clock is ticking, however and they need
to move now before they miss out on the
advantages of being an early AI adopter.
The truth is that many retailers haven’t quite
figured out how to execute their AI strategy
yet or are still undecided on the first steps
they need to take to introduce AI successfully. But, by following just a few key steps,
retailers can adopt AI technologies with
ease, to increase profit margins and optimise
business performance:
www.intelligentcio.com