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Payments Analytics – Analytics to Make Decisions

 

Balamurali Srinivasan ( Bala) leads the Digital CoE Practice in iGTB.  Bala has 24+ years of experience in banking product technology. As a part of the Digital CoE, his key area of focus is  leading a very talented team working on the contextual experience services, AI/ ML driven analytics, UX design and DevOps of CBX product https://www.igtb.com/platform/  .

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Payments Analytics – Analytics to Make Decisions

PABLO PICASSO ONCE SAID: “Computers are useless. They can only give you answers.”

One interpretation I like about this quote is that it isn’t the answers which are important or hard. Answers could be seen as being relatively easy, once you know the question. Asking original questions takes creativity and insight, and pushes the frontiers of our world further.

Businesses are now realizing that they have to move from the era of “generic data visualization” to that of “insightful questioning”.

Decision-driven data analytics emphasizes the importance of asking questions and thus the importance of judgment. The question we ponder about in our real life is – if I do X, can I get Y? Insightful analytics is all about questions and data helping make decisions.

Analytics is normally just done with the data at hand. However that data may be incomplete.

Imagine a gym which has developed a rich data set that describes past and current customers along various dimensions, such as location, timing of attendance, duration, and nature of fitness classes. The gym has also gone beyond the tradition and has developed a predictive algorithm that uses this information to quantify the likelihood when a member is going to be inactive. One can reason that we don’t need algorithms and grey hair wisdom will tell you that membership is high during New Year resolutions phase and tapers down gradually.

However, jokes apart, the fundamental question that the gym business owner should answer is “What incentive can I offer to the member so he does not churn?”.

This question may not be answered based on the data the gym has already gathered and would require further data collection and analysis.

To make this decision, the gym needs to do a sampling, a randomtest … and then apply the algorithm. So while data is important, it is super important that we find data for a purpose rather than find a purpose for the data ( Cited from Decision Driven Analytics)

Shifting our focus to fintech, let us see the ROI of analytics on payment transactions.

Contextual CX – (Customer Experience)

Banks currently have little or no insight into the business purpose of a payment. There is a blind spot on how customers are similarly restricted of the intelligence – for example, spending patterns or habits – they can extract from their own payments data. In both cases, insufficient or poor quality data limit a bank’s ability to develop relevant, valuable and personalized new services for customers. The rich end-to-end data that ISO 20022 provides can help in developing more compelling value propositions for customers.

Simple Code assessments of transaction purpose categories (e.g. SALA for salary payments, INTE for interest payments, TAXS for tax payments, SUPP for payments to suppliers and CCRD for credit card payments) from ISO 20022 messages enable the analysis of categories and the frequency of transactions. This in turn makes it easy to identify cross-selling and up-selling opportunities.

Improved compliance

There is historical data to prove the necessity of improved compliance- 10% of payments typically stopped by a sanctions filter, triggering an investigation and mostly leading to false positives. Refer pain.013 XSD 

In the ISO 20022, for example, the postal address (PstlAdr) unambiguously identifies the country. Accurate country information also allows risk and compliance managers to understand precisely where the money originates from and where it goes in the payments the bank handles. Banks can also accurately identify other key information such as postcode (PstCd), which can for example be used to build up demographic data for analysis.

Deeper KPI(s) on Payment

pain.001.001.XX, pacs.008.001.XX, pacs009.001.XX and pacs.010.001.ZZ, are formats which contain business fields such as payment method, number of transactions, amount, requested execution date and requested collection date in message types. This gets the radical insights into the average values and volumes of payments made by each corporate customer and to understand payables and receivables over a specific period of time. As our corporate customers continue to expand operations into new markets thereby boosting the volumes of domestic and cross border transactions, the bank can derive insights and help them with easy access to liquidity, and to mobilize this across geographies becomes ever more important

Balamurali Srinivasan ( Bala) leads the Digital CoE Practice in iGTB.  Bala has 24+ years of experience in banking product technology. As a part of the Digital CoE, his key area of focus is  leading a very talented team working on the contextual experience services, AI/ ML driven analytics, UX design and DevOps of CBX product https://www.igtb.com/digital-transaction-banking/

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