BasicOverview
With the help of location intelligence maps, financial organizations can receive a deeper understanding of where their users and clients use their services. Location intelligence tools can provide the data needed to build a truly customer-focused service, with security and fraud-prevention also benefiting from a location intelligence framework.
Challenge
A bank in Bogota, Columbia, is planning to expand its ATM network with cardless ATMs across the city. They want to test this new approach and collect feedback from customers in order to decide if they need to move forward with the project’s development and implementation. Due to the fact that the bank wants to test the cardless approach on only a limited number of ATMs, their location needs to be chosen very carefully.
Our solution
Aspectum created a map of the Bogota metropolitan area, then added anonymized ATM transaction data, showing the amount and date of each ATM transaction for two months in 2019. Next, neighborhoods were colored in depending on the number of transactions there. Using a line graph, it is possible to track whether there was a decline in the total amount of transactions on the dates specified. The histogram allows to filter the quarters by the total number of transactions there. Four (two for quarters, two for transactions) numeric indicators interact with the rest of the widgets and display the number of transactions and the total amount.
To have a more comprehensive view, we apply grid analysis with 500m radius to see where the biggest amount of transactions were made.
Result
Grids of different heights specify areas with the most transactions, making them the perfect place to pilot the bank’s cardless ATM project. Data received from the project can be further input into the map, to generate insights useful for expansion considerations. The multi-zoom functionality lets users switch from a general view of the city to the neighborhoods where identified areas can be analyzed more precisely.
The map can be further enriched with other data, for example, to locate underserved neighborhoods to open new branches in.
Value for
Financial companies, consulting agencies
Key Technical Features
- Categorical Filtering
- Time-Series Widget
- Search by Column Value
- Multiscale visualization