VTB has developed a technology for analyzing the effectiveness of bank branches

2024: Development of a business intelligence tool to assess the performance of regional offices

VTB has developed and implemented a business intelligence tool to assess the effectiveness of regional offices. With algorithmic machine learning, the service analyzes, visualizes and identifies key variables that affect financial results. Based on the data obtained, the bank can make decisions on opening branches in new places with high cross-country traffic and the need for financial services from customers or closing unprofitable ones. The bank announced this on June 14, 2024.

The service "Analysis of generation indicator PL" (profiles and losses - forecast indicator of expenses and income) allows you to compare and interpret a large amount of information from different points, identify general trends and determine effective strategies.

To implement the project, specialized data storefronts were organized containing information on operating activities, client interactions and financial indicators of the retail business. "Generational Branch PL Analysis" operates on the basis of retail business data accumulated over three years and containing financial, operational and customer metrics.

For a particular point of sale, the system automatically selects the 30 most important of the more than 300 possible valuation parameters presented in the overall data mart. Models analyze the impact of client service metrics: service time, queue percentage, number of unmet users with cause characterization, and many other parameters to highlight the most significant deviations that affect PL.

As of June 2024, about 1.3 thousand VTB branches operate in the regions of Russia. We are thoroughly reviewing the approaches to developing the network in order to make the work of each office even more efficient and comfortable for customers. The presented forecasting technology will contribute to this and will allow achieving strategic indicators, including a 40% increase in the number of operating branches, "said Ruslan Eremenko, member of the VTB Board.

Relevance, completeness and correctness of data are key characteristics necessary for analytics and building machine learning models. This information, as well as modern data processing tools, made it possible to create a system for analyzing the effectiveness of bank branches aimed at optimizing work, "commented Nikita Rybchenko, Head of the Technological Development Department of General Banking Systems, Senior Vice President of VTB.