Potentials - November/December 2017 - 23

well. However, because we worked
with them from the early stages, we
believe that our customers are the
first in the market to benefit from
the solutions. Not only do the solu-
tions help us retain and increase our
customer base, they also help gener-
ate new revenue from channels en-
abled by these innovative solutions.
IEEE Potentials: Can you share
with us the story of one of your
start-ups?
TOV@OCBC: We have a start-up
that is currently working in our co-
working space called Onelyst. Onely-
st was part of our inaugural batch
of start-ups who participated in our
accelerator program in 2016. Onelyst
is working toward providing moneylending solutions to people from low-
er-income groups. People from such
groups tend to have lower credit
scores. As banks and financial insti-
tutions rely heavily on credit scores
in assessing candidates for the grant
of loans, their low credit score may
make it difficult for these people to
get loans.
Onelyst tries to create credit scores
by using alternative parameters than
those used traditionally. It then works
with money-lending organizations
to provide small loans to customers
based on these credit scores. For in-
stance, a customer can get a small
loan to buy a refrigerator from a re-
seller with whichever bank the re-

We would like to help our customers realize
their dreams, and data help us to understand
how we can actually do it.

seller has tied up with. For instance
a customer can get a small loan from
a bank named , say "X," if the shop
selling the refrigerator has a tie-up
with bank "X." The bank "X" or the
reseller or both could be Onelyst cli-
ents. Onelyst also works directly with
the resellers and organizations to en-
able its users to have access to loans
as well as products that they may
need for themselves or their families.
IEEE Potentials: What kinds of
technologies do you feel are interest-
ing for the banking industry? There
are technologies like machine learn-
ing, data analytics, blockchain, and
others that are being talked about
these days.
TOV@OCBC: We find that ma-
chine learning and data analytics
are getting a lot of traction these
days. Data analytics is particularly
helpful because it helps glean in-
formation from the vast amount of
data that we have with respect to
our products, offerings, and cus-
tomers. It allows us to tailor solu-
tions for specific customer segments
as well as to reach out to them on
a more informal and personal level.

We would like to help our customers
realize their dreams, and data help
us to understand how we can actu-
ally do it.
Machine learning or artificial
intelligence is also getting a fair
amount of attention, but we are fo-
cusing on areas where we can define
the scope of the problem very specif-
ically. For instance, we have a chat-
bot named "Emma," which engages
with our existing and potential cli-
ents looking for loans. By focusing
Emma on providing information and
help related to loans, we have been
able to see success. Emma has been
able to close loans with customers
worth tens of millions of dollars
since its pilot. We have now gone
ahead and integrated it completely
with our website.
Lastly, blockchain is also an in-
teresting technolog y, a nd we a re
evaluating it to see if it can simplify
the processes related to "Know Your
Customer" guidelines stipulated by
the regulatory agencies.

Read more about it
	 *	 K. D. Garbade and W. L. Silber,
"Technology, communications and
the performance of financial mar-
kets: 1840-1975," J. Finance, vol. 33,
no. 3, pp. 819-832, 1978.
	 *	 (2017, Aug. 24) Chat with "Emma."
[Online]. Available: http://www.ocbc
.com/persona l-ba n k i ng/loa ns/
home-loan-hdb-and-private-property
.html

About the author
Sharad Sinha (sharad_sinha@ieee
.org) is an associate editor with IEEE
Potentials. He is a research scientist
with the School of Computer Science
and Engineering, Nanyang Techno-
logical University, Singapore.
The Onelyst team.



		
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