OTP Bank has introduced neural networks to optimize personnel selection

2024: Introducing AI into the process of finding and recruiting employees

OTP Bank September 20, 2024 announced the introduction neural network of technologies in the process of searching and recruiting employees to improve the efficiency of recruiting and optimization. business processes bank

Digital tools help OTP Bank to accelerate data collection, analysis and processing, which means making more informed decisions as soon as possible. As part of the digital transformation strategy, the bank introduced a recruitment resume matching model based on machine learning technologies and large language models (LLMs).

This model allows you to automatically perform the initial stage of searching for suitable candidates when processing recruitment requests, using the accumulated information about already considered applicants. In the future, this will reduce the time for closing vacancies, reduce the cost of hiring and increase the overall efficiency of recruiting. The developed model analyzes the selection request, highlighting key meanings, after which the resume base is filtered and compared. As a result, a list of the most suitable candidates is formed, which is received by the bank's recruiters.

We are confident that artificial intelligence has great potential to optimize business processes and increase the efficiency of all bank functions, "said Natalya Roshchina, HR Director of OTP Bank. - Thanks to the joint work of the HR and ML development teams of our bank, it now takes only a few minutes to find the right candidates in the extensive internal database.

The introduction of neural network technologies in the recruitment of personnel is part of the long-term strategy of OTP Bank to improve the operational efficiency and accuracy of forecasts. The Bank continues to invest in modern technologies such as machine learning and analytical platforms to improve the quality of decision-making.

The Bank's OTP team does not intend to stop there - it is working to create a system of "early signals" about changes in employee productivity and forecasting their tendency to burn out, as well as automating the preparation of personnel documentation using large language models (LLM).