The attraction of ISO20022 messaging data is:
-It is unambiguous: the meaning of each data element
is known
-It is credible: the origin point of the data is
known.
-It is consistent: all ISO messages follow a
standard that is universally understood.
These factors lend itself to data analytics at scale. Many
banks have data lakes and initiatives to use AI/ML to develop insights. The
building blocks are in place.
What is payments data?
Data generated by providing payment services such as card
payments, mobile payments, debit and credit transfers, ATM transactions etc.
The type of information about the persons doing the transactions collected are PII
data, sort and account codes, payment date and time as examples. Enriched data
that is not necessary for payment processing such as location, mobile device
id, cookie for online store, channel, frequency of use are other features that
can be incorporated while developing insight from the payment data.
Use case for these analytics
-Internal
Operational efficiency such as cash loading cycle for ATMs,
designing reward programs for encouraging a specific banking channel, loan
underwriting feeds, better AML/KYC to start with and these can go deeper as the
bank becomes familiar with exploiting the use cases and repurpose for other areas
-External
Developing premium products such as integration to
corporate ERP systems for real time cash flow analysis, treasury collateral
forecasting, sharing purchasing behaviour with customers is helpful for
predicting seasonality in purchase, value or impact of a promotion or sale offers.
These offerings build deeper touch points with customer and ring fence the
customer from competition by fintechs.
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