Modelling of client policy elements of the regional commercial banks on the basis of digital methods of data processing

Abstract

The concept of the research is based on the assumption that the improvements in the customer policy of a commercial bank should be focused on the development of methodological tools of assessment and modeling of its elements as well as on the implementation of arrangements contributing to higher quality of customer care Their importance increases in the conditions of economy digitalization in general and bank service, in particular. The authors consider customer policy from the perspective of the polarity of its elements that include banking service processes and customer loyalty and satisfaction indexes. The latter can be assessed taking into account quantitative and qualitative information. The article suggests the algorithm to identify the potential change in the interest rates on loans and deposits with the use of Price Sensitivity Meter (PSM) which enables banks to keep relevant price offers and neutralize mass customer attrition. The suggested algorithm is based on the comparative analysis of the current tariff rates and customers' price preferences as well as their demand for services and solvency. The calculation of CSI (Customer satisfaction index) allowed the authors to assess customer satisfaction levels in commercial banks of the North Caucasian Federal District and identify negative factors influencing the choice of credit organizations by companies and individuals. The methodology of quality control of customer care in the regional commercial banks including computer-aided tools, latent semantic analysis, text mining and other methods of digital data processing was developed Its implementation resulted in the frame of interrelated concepts through structuring customer feedback and its automatic monitoring, which formed the basis for the organizational and functional model of customer service The modeling of customer base of a commercial bank based on spatial structuring of information about customers (cross-sectional data) and time-series data analysis allowed the authors to reveal the impact of hidden or unaccounted factors on the activities of bank customers and assess the probability of their bankruptcy accounting for intra-group and individual peculiarities