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Anumber of buy-side and sell- side firms have come together
to explore using Blockchain technology could be used to improve
reference data processes.
The group, which is working
alongside technology firms R3
and Axoni through the Securities
Industry and Financial Markets
Assocation (SIFMA), has recently
complete a proof of concept
exercise that was coordinated by
Credit Suisse to build a distributed ledger prototype that would
enhance risk management, cost
and efficiency for reference data.
The concept was developed using
Axoni’s proprietary distributed
ledger software to simulate collaborative management of reference data.
Participants in the test were able to
interact with the reference data after
corporate bond issuance. Any proposed changes to the data required
validation by the underwriter to give
the ledger a single, immutable record
of all data related to the bond.
The firms claim that up to 70%
of the data used in financial transactions is reference data, underlining the importance of ensuring
such datasets are accurate.
Dean Rutter, CEO of R3, said:
“Quality of data has become
a crucial issue for financial
institutions in today’s markets.
Unfortunately, their middle and
back offices rely on legacy systems
and processes – often manual – to
manage and repair unclear, inac-
curate reference data. Distributed
ledger technology – which allows
financial institutions to push these
functions to a cloud environment
– removes the need to reconcile
multiple copies of data, providing a
sophisticated and agile solution to
the headaches currently caused by
these legacy systems and processes.”
While the initial prototype sug-
gests Blockchain technology could
have applications in reference
data, it remains in its early stages,
though participants in the proof
of concept hope it will encourage
further review of how it can be
applied to improve efficiencies in
data and other financial services
Reference data latest area to
benefit from Blockchain tech
Proof of concept led by Credit Suisse suggests
Blockchain tech can be applied to data.