Case study

Why is it so difficult to extract insights from transaction data?

Case study

Why is it so difficult to extract insights from transaction data?

The results
A major international bank found gini to be the most efficient data enrichment provider for its digital banking upgrade initiative.

In a pilot project with gini, the bank ran 50,000 transactions through our data enrichment engine.  Within 72 hours, gini had enriched 95.7% credit card transactions and 92.7% EPS transactions. 

“We were surprised just how fast gini’s enrichment capabilities are. What we expected to take 3 weeks took them only 3 days,” said the bank’s Head of Innovation and Strategy. “On top of that, they even enriched EPS transactions, which no other provider has achieved.”
Credit card
transactions enriched
EPS
transactions enriched
The results
gini introduced a successful Savings Goal feature in our PFM app that was adopted by 60% of users within 30 days of launching.
 
Our users engage with the Savings Goal feature an average of 7.4 times a month, which when compared to the once-a-month engagement of most banking apps, is a testament to its value. 

And the reviews were overwhelmingly positive, with comments such as, “Congrats on the release of the saving function, it’s very helpful and motivates me to save more!” and “Makes saving and budgeting a lot easier.”

Makes saving and budgeting a lot easier.
The challenge
Our research showed that users wanted a savings feature that automates their budgeting calculations, and shows how much they have left to spend after putting their savings aside every month.

However, no PFM apps in Hong Kong had a feature like this because it requires complicated algorithms and enriched transaction data. Without merchant names for example, it’s difficult to label recurring transactions accurately, and give the user a clear, comprehensive overview of their finances.

The solution
With data automatically enriched by our machine learning models, gini was able to build a fully functioning Saving Goals feature that resonated with users and increased engagement.

The new feature automatically calculates a monthly OK to Spend amount by subtracting the user’s total monthly expenses (past and upcoming) and Savings Goal from their total monthly income. It also has a traffic light system that warns users when it’s time to reign in their spending.

None of this was possible without first enriching the transaction data with accurate merchant names and categories.
The challenge
A recent digital banking survey showed low levels of satisfaction, with 87% of customers finding it hard to understand their transaction feeds.
My current spending history is confusing. I want to see the ACTUAL shop name.
To address this — and reduce queries — the bank planned to first replace standard transaction codes with clear merchant names and categories throughout its digital banking services. And then to increase loyalty with a personal finance app, built on the foundation of enriched data. 

However, developing the technology to transform such large volumes of transaction data was proving to be a Herculean task — one that would take years. So they looked for an external provider to help clean, structure and enrich the data accurately and quickly.

The solution
Impressed by the quality and speed of gini’s enrichment engine in the pilot project, the bank plans to integrate our scalable software into their own systems to allow for real-time data processing and enrichment. The best part is, gini’s technology is easily accessible as a SaaS solution on AWS Marketplace, avoiding the need for lengthy tech stack integration processes.

Soon, the bank’s entire customer base will have their transaction feeds transformed from confusing codes to recognisable merchant names, logos and categories. This is predicted to have a significantly positive impact on NPS scores.

Equipped with enriched data, the bank’s development team will then be able to build a competitive personal finance app with much richer features than otherwise possible.
Contact us to find out more about our digital banking data solutions
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Open banking in 2020: Are you ready?

Open banking is primed to become the new norm in Asia Pacific. But, as our research report shows, the majority of bankers in the region are not sufficiently prepared for what’s coming.

It’s time to get smart on what open banking is and how it’s expected to impact the market this year. 
gini's original research report on open banking in Asia Pacific for 2020
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Download the open Banking 2020 research report by gini
We interviewed more than 300 finance and technology thought leaders across Asia on the industry’s readiness for open banking this year, with surprising results. 
Download our Open Banking 2020 research report to find out: 

The opportunities in store for all participants
The barriers to adoption
Who is expected to benefit most 
How institutions can generate revenue from open APIs
And more
Why is it so difficult to extract insights from transaction data? - Header image

Why is it so difficult to extract insights from transaction data?

By
on
Aug 26, 2020

Despite being extremely valuable in terms of consumer insights, raw transaction data is notoriously difficult to make use of. 

The insights hidden in transaction data offer enormous opportunities. By analysing how customers spend, financial institutions can understand their needs both now and in the future, and build personal relationships with individual customers at scale through efficient digital services. 

But why is the data itself so challenging to work with?

There are several factors contributing to the problem:

  1. Each transaction description is made up of information from multiple sources
    Each piece of raw transaction data is made up of a complex web of information from multiple entities, including the merchant’s point-of-sale server, the payment scheme provider, some middleware and security software, the issuing bank, the acquiring bank and several other devices.

    As a result, the data often ends up as a string of unintelligible letters, numbers and symbols that makes it difficult to decipher, both for banks and for the consumers themselves. 
  1. Merchants are often registered as their holding company names
    Merchants often don’t have a choice about how their name appears on statements. When they first apply for a POS terminal, it’s the processing centre that decides how to register the merchant. More often than not, the name of the legal holding company is used (e.g. “The Dairy Farm Company Limited”) instead of the trading name (e.g. “Wellcome”).  

    This makes it very difficult for financial institutions to analyse exactly where, and in what categories, consumers are spending. It also prevents banks from extracting the kind of granular insights that can be used to generate targeted offers, as the distinction between “Mannings” (health and beauty) and “Mannings Baby” (maternity) for example, is lost.
Customer confused by unclear transaction descriptions
  1. Information is rarely updated when merchants move or change their name
    This happens all the time. What was once a printers shop is now a cafe. A restaurant owner may change the name and concept of the restaurant as many times as needed. Businesses open, close, and change constantly. And most of the time, it’s cheaper just to take their existing payment terminals with them rather than registering new ones. 
  1. A lack of industry standardisation
    Visa, Mastercard, Amex and other payment scheme providers all have their own way of recording transactions, which means there is no consistency in how a transaction appears on a statement. As a result, there are countless ways one card-accepting merchant can appear in a transaction description.
  1. The transaction description has a limited number of characters
    The transaction description itself is limited to around 23 characters or so. Therefore, the message sent from the merchant’s acquiring bank to the consumer’s issuing bank is often incomplete. When a transaction is queried, the bank often has no more information than the customer does. 

The problem of unintelligible transaction data is costing the finance industry millions every year in customer support and chargeback and fraud investigations. 

It’s also preventing banks from harnessing the power of big data analytics to deepen relationships and attract new customers, which could boost profits by 20% to 40%, according to a report by McKinsey & Company.

Comparison between confusing non-enriched and clear enriched transactions

gini uses machine learning to transform unintelligible raw transaction data into clean datasets rich in consumer intelligence. Instead of a string of numbers and letters, banks get an accurate merchant trading name, category and location, along with descriptive tags. This allows for much more efficient analytics and much richer consumer insights.

To find out more about how we help financial institutions harness the full potential of their transaction data, click the link below.

What is data enrichment and why is it so important?
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