In the history of your card transactions, it may not be easy to remember some of them because of an insufficiently clear description. Usually it contains |Merchant name|City|Country|.
Example: |MCDONALD'S F7329|ANAHEIM|US| OK, it's McDonald's in Anaheim, but which exactly?
The automated processing of the address on the bank side is complicated by the fact that the same address can be represented in different ways.
Example: "NEW YORK", "NEW YORK CITY", "NEW YORK, NY", "NEW YORK-CITY", "NEWYORK", "NEWYORKCITY", "NewYorkCounty", "NEY YORK", "NNEW YORK", etc. It's all the same city!
Sometimes the address cannot be identified exactly at all.
Example: "N.York." is it "New York" or "North Yorkshire"?
But the worst case is when it is almost impossible to understand what kind of place it is.
Example: | |NEW YORK|US| or |MM6|NEW YORK|US|
In addition to the name and address, bank gets different identifiers of: device, partner, acquirer.
Based on these data our solution allows to get the geographical coordinates of the place where the transaction was made.
And it does not matter what kind of device is it (ATM or POS-terminal), in which country it is located and which bank it serves.
(the bigger circle, the bigger count of transactions)
Viewing history of card transactions will be interesting both to the client (it's easier to remember each of the transactions) and to the bank's employees (for example, for parsing claims).
After processing the transaction history, we can tell the bank where each client lives, where he works, which other clients he knows.
The obtained information can be used, for example, to adjust credit risks (taked into account the profiles of the client's friends, track the shift or loss of work), to increase the sales of credit products (to offer a loan when the client went on vacation), to carry out marketing campaigns with partners.
Summarizing the data for all clients, it is possible optimize the development of the network of branches and ATMs.
And of course this is not all the possibilities.
Unlike the analysis of clients based on data from open sources (for example, social networks), the proposed solution does not require transferring any client data beyond the bank.
That minimizes the risks of theft of this data and the risks of losing customers, and also reduces dependence on third-party companies and possible legislative restrictions on work with them.