Page 29 - Banking Finance April 2022

ARTICLE

Y AI-powered virtual assistant "iPal" of ICICI
Bank can handle queries on GST,
government digital initiatives, ATM locations,
products & services, branch particulars and
IFSC codes. Kotak Mahindra Bank's "Keya"
can recognise customer intent and can
independently call serviced by the voicebot
without any human intervention. HDFC
Bank's e-virtual assistant "Eva" like so many
other chatbots follows the industry norm in
being female, can also handle FAQs. She has
been integrated with online travel
aggregators and service providers, so she
can book bus tickets or chart out trips for
customers.

AI is the future
In the AI-powered digital world, banks will
endeavour to meet customers' rising
expectations and beat competitive threats.
Hence, AI-equipped Banks will offer propositions
and capabilities which will be:
1. Smart and intelligent- Recommending measures,
predicting and automating key decisions. (Source: Wipro)
2. Custom-made- Pertinent and timely, and based on a Use Cases of Analytics in Banking
comprehensive understanding of customers' past
behaviour. 1. Customer Experience- Analytics can be used for
customer segmentation. Using Big Data, banks can
3. Omnichannel- Impeccably covering the physical and
segregate clients based as per their demographic
online contexts across multiple devices and delivering a profiles, behaviour, including buying or investment
consistent experience.
patterns. This will benefit the banks in marketing to
target audiences and building better customer
The AI-equipped bank of the future will also enjoy the speed
and agility that today characterize digital-enabled relationships. Banks can also analyse the spending
companies. It will innovate promptly, launching new features patterns of their clients using predictive analysis. This
in days instead of months. It will collaborate significantly will help to identify when potential customers may need
with partners to integrate perfectly across journeys and specific financial services.
technology platforms to deliver robust output. 2. Risk Management- Risk assessment is of high priority
for banks, as it helps to regulate financial activities and
Data Analytics in Banking in the pricing of financial investments. Risks come in
Presently, with banking products and services becoming many forms e.g., bad loans, fraudulent activities, or
increasingly commoditized, data analytics can help banks investments that have flopped. Also, banks have been
distinguish themselves and gain a competitive edge. Data under great strain due to increased competition from
is something that banks have been dealing with for so many non-banking players, low asset yields and an increase
years, and its scientific analysis can help them bring about in commercial borrowings. These factors signify risk for
major enhancements in performance. Analytics can also the bank and early detection of these risks can help a
provide the management with valuable inputs at each stage bank avert a major loss. Thus, Analytics can help in
in the customer lifecycle. mitigating risks.

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