Subject. The paper considers planning of resource products in the corporate business segment of a commercial bank. Objectives. The aim is to develop a model for planning financial results generated by attracted time deposit accounts and demand accounts of the corporate business segment in a commercial bank. Methods. I employ mathematical, statistical, and econometric methods and applied programming methods (SARIMA model for planning the profile of resource products of corporate clients, taking into account seasonality; the Ward's method for clustering corporate clients to create a pattern of financial behavior; SQL language for building uniquely designed models of resource product planning). Results. I identified the main drivers, algorithms and pricing principles of resource products. On their basis, I developed my own models for planning financial results from attracting time deposit accounts and demand accounts of the corporate business segment in a commercial bank. The uniquely designed planning models were tested on the basis of the medium-sized corporate business segment of one of the largest Russian commercial banks. Conclusions. The use of proprietary models enables to increase the accuracy of financial planning. The definition of a corporate client as the main driver contributes to the construction of a customer-oriented corporate customer service system, develops an incentive system, and allows to create additional incentives to increase cross-sales of banking products.
Keywords: current account, urgent attraction, resource product, financial planning, commercial bank
References:
Bachinin Yu.G., Veinberg R.R., Romanov V.P. [Customer segmentation for a telecommunication company based on wavelet transform clustering of a customer’s profile]. Nauchnye trudy Vol'nogo ekonomicheskogo obshchestva Rossii = Scientific Works of the Free Economic Society of Russia, 2012, vol. 164, pp. 140–149. URL: Link (In Russ.)
Davis R., Nielsen M. Modeling of Time Series Using Random Forests: Theoretical Developments. Electronic Journal of Statistics, 2020, vol. 14, pp. 3644–3671. URL: Link
Elliott G., Timmermann A. Economic Forecasting. Princeton University Press, 2016, 568 p.
Granger C., Machina M. Forecasting and Decision Theory. In: Elliott G., Granger C., Timmermann A. (eds) Handbook of Economic Forecasting. Elsevier, 2006, vol. 1, pp. 81–98.
McAleer M., Medeiros M. Forecasting Realized Volatility with Linear and Nonlinear Univariate Models. Journal of Economic Surveys, 2011, vol. 25, iss. 1, pp. 6–18. URL: Link
Turnbull P.W., Gibbs M.L. Marketing Bank Services to Corporate Customers: The Importance of Relationships. International Journal of Bank Marketing, 1987, vol. 5, iss. 1, pp. 19–26. URL: Link
Cornwell B. Linkage Criteria for Agglomerative Hierarchical Clustering. In: Social Sequence Analysis: Methods and Applications. Cambridge University Press, 2015, pp. 270–274. URL: Link
Dickey D.A., Fuller W.A. Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 1979, vol. 74, no. 366, pp. 427–431. URL: Link
Smithson M., Verkuilen J. A Better Lemon Squeezer? Maximum-Likelihood Regression with Beta-Distributed Dependent Variables. Psychological Methods, 2006, vol. 11, iss. 1, pp. 54–71. URL: Link
Bobrovnikov A.E. Finansovoe planirovanie i byudzhetirovanie [Financial Planning and Budgeting]. Moscow, 1S-Pablishing Publ., 2018, 313 p.
Bogdanov D.V., Shapiro I.E. [Problems of development of digital technologies in banking system of Russia]. Vestnik Rostovskogo gosudarstvennogo ekonomicheskogo universiteta (RINKh) = Bulletin of Rostov State University of Economics (RINH), 2019, no. 2, pp. 161–165. URL: Link (In Russ.)
Budanova M.M., Peresvetov S.B. [Research of insurance markets of 31 countries based on insurance indicators and identification of Russia's place in the cluster hierarchy using cluster analysis in the R environment]. Korporativnye finansy, 2017, vol. 11, no. 2, pp. 96–115. (In Russ.) URL: Link
Gasanova M.R. [Development of approaches to planning of commission income of a commercial bank in the corporate segment]. Innovatsii i investitsii = Innovations and Investments, 2021, no. 8, pp. 82–91. URL: Link (In Russ.)
Gasanova M.R. [Formation of approaches to the construction of a financial model for the purposes of planning a credit business in a commercial bank in the corporate segment]. Voprosy istorii, 2021, no. 8-2, pp. 113–129. (In Russ.)
Zaguzina E.G. [Comparative analysis of statistical methods and neural network technology in the problem of classification of clients subjects of small business]. Vysshaya shkola, 2016, no. 17, pp. 10–12. URL: Link (In Russ.)
Neiskii I.M., Filippovich A.Yu. [Design tariff policy for brokerage service clients based on adaptive clustering methods]. Prikladnaya informatika = Applied Informatics, 2011, no. 1, pp. 3–11. URL: Link (In Russ.)
Rud'ko-Silivanov V.V., Naumov A.A., Yakukhnyi E.M. [Forecasting financial indicators of the activity of credit organizations]. Den'gi i kredit = Russian Journal of Money and Finance, 2013, no. 2, pp. 52–58. URL: Link (In Russ.)
Atrill P., McLaney E. Finansovyi menedzhment i upravlencheskii uchet dlya rukovoditelei i biznesmenov [Accounting and Finance for Non-Specialists]. Moscow, Al'pina Pablisher Publ., 2012, 648 p.
