VTB, Russia’s second largest bank, has begun using artificial intelligence to determine the ideal locations for branches and ATMs.
The bank has developed and implemented a machine learning (ML) model that uses Big Data analysis to predict the demand for banking services in specific locations of the city.
More than 5000 parameters are used to determine the potential number of customers and sales volumes in new locations. These include the population of the region and the proximity of the branch to shopping centers and transport stops.
Each of the algorithms is used to work with specific data. For example, one of them enables the evaluation of changes in customer behavior during the renovation period of a branch, while another chooses the optimal location for a new branch for both the client and bank in terms of convenience and accessibility.
Using the new model, VTB performed calculations for all major Russian cities where it works with retail customers. Soon, the bank will complete calculations for the entire network.
Anatoly Pechatnikov, deputy president and chairman of the VTB management board, says: “Artificial intelligence analyses thousands of factors and allows us to provide an optimal result in a short time. This is very important when planning the development of a network of branches, especially in cities with the highest density of bank offices.
“In three or four years, the new model will allow us to modernise a third of the retail network so that the average accessibility of offices for clients is no more than 15 minutes, due to their optimal location.”
