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Financial transactions are one of the most valuable sources of information available to banks, fintechs and any other organisation accessing them through open banking.
And yet, they’re often difficult to understand - even for the customer who made them.
Transforming transactional data into genuine insights requires enriching them with information that provides context, allows for an instant understanding of the transaction and unlocks a variety of use cases effectively – from superior user experiences to advanced risk analytics.
Through enrichment, organisations can pinpoint and categorise transactions, surface the identity of the merchant, find the payment processor and - in some cases - detect the location where a transaction took place.
By using all available information and matching it to a location database, location detection provides a uniform output which can then be analysed and used.
In cases where an exact match is found, a form of geolocation coordinates can be returned but the results can also return less specific information such as city, country, state or country. In some applications, however, even the country's information might be valuable and so core enrichment becomes vital for success.
The information available for detection can be split into direct location indicators that are present in the transaction text (e.g. postcode or street name), and information derived from other enrichments.
One of the most important enrichments, for example, is merchant identification because in many instances, it’s possible to use a variety of signals from the original transaction to pinpoint a specific branch or location. This information can include branch codes, partial address indicators and other contextualised insights that enable the engine to find the corresponding merchant database entry and produce the exact location.
Location information is only available for physical transactions. Online payments, most non-merchant transactions and money transfers cannot be mapped to a specific place.
In fact, many transactions where location is applicable don't actually contain any helpful information. For example, if a transaction has taken place at a chain store with hundreds of locations, and the transaction description has only the brand name and nothing more, it won’t be possible to identify the location (perhaps beyond country or state if it is a regional brand).
For transactions where some indication of location is available, the level of location granularity can vary depending on the type of data available. Because location identification is hierarchical, we always strive to access it at the most detailed, granular level - geo-coordinates pointing to a specific address. If that isn’t possible, the fallback scenario is to identify some of the information and attempt to match it to a corresponding entity, whether that’s a city or a state.
Because providing geo-coordinates for a state can be misleading, our engine will always indicate what type of location match is provided (exact match, city, state etc.). For any partial matches, i.e. just city, we can return geocoordinates that would point to the city centre, meaning it’s still possible to both present and process that information on the map in the same way as the exact location depending on the use case.
Location information enables multiple use cases that benefit both the customer as well as the financial institution.
Being able to present location information on a map when displaying transaction details creates a much more engaging user interface.
When combined with a merchant logo, website, categories, a short summary of similar spending or even total spend with the merchant, it can boost the level of customer satisfaction. This makes digital channels far more sticky and useful but also extends beyond simple user engagement.
By providing address or location information, institutions can significantly improve the chances of minimising potential disputes based on unrecognised transactions as visualising where a transaction took place makes it easier for a user to identify it.
This is especially useful with transactions from commonly frequented merchants, where just the brand name isn’t enough to make a distinction between each transaction and whether or not it was the customer who made it. Given that dispute management is a major cost centre for many banks, there is a tangible value to this scenario for financial institutions across the globe.
While it’s not possible to geolocate every transaction, it might be still possible to catch things that are widely out of pattern. Given that our location identification is a real-time process that takes milliseconds, it is entirely feasible to screen the currently authorised transaction and its location against the known location of the customer.
Because location can be derived from a combination of factors - including app location and the locations of previous transactions - it’s easier to proactively identify and mitigate fraud. For example, if a series of transactions occur with a new merchant or in a different region, it’s likely a strong indicator of fraudulent activity which you can then take action against.
With added context into location, organisations can better understand their customers at both an individual level or a holistic one - resulting in better financial outcomes for all. Being able to identify areas where customers are frequently spending translates to a good picture of where they are during weekdays and weekends, work locations and more.
This enables a variety of applications, from understanding how to develop a physical presence that matches the locations of your customers to more advanced scenarios like understanding commitment habits and being able to take that into account - e.g. when tailoring a mortgage offer.
Given that many financial institutions have plenty of partnerships with merchants, being able to create joint, more tailored reward schemes that fit the needs of customers is a great way to build up loyalty and generate new revenue. That’s because analytics into your customers combining location, spending and profiles is invaluable information for any brick-and-mortar business.
Very closely related to our ability to detect entities and merchants, Bud’s location enrichment is a feature currently being rolled out to new markets. Location detection is a very useful form of transaction enrichment, which can be readily used by any financial institution – regardless of whether it uses in-house categorisation or not, it’s a feature any modern digital banking experience would benefit from.
Because we expect location-based scenarios will be expanding, with an increase in digital wallets that heavily rely on mobile apps, NFC payments and more; our engine can also ingest external location information with ease. This means that when enriching NFC payments we are not dependent only on data available in the transaction text but we can easily match transactions with known user location information.
Looking to get started with location enrichment? Speak to a member of our team and discover how Bud can transform how you engage with customers at every financial touchpoint.
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