Financial Data Analysis for Financial Management Based on Cloud Computing Using Deep Reinforcement Learning Model

Parviz Gurbanov^1,*, Mansur Matkarimov², Nilufar Sapayeva³, Alexey Nedelkin⁴, Andrey Kulik⁵, Olga Zanina⁶

¹Department of Economics, Baku Eurasian University, Baku, AZ1110, Azerbaijan

²Department of Economics, Mamun University, Khiva, 220900, Uzbekista

³Department of Business and Management, Urgench State University, Urgench, 220100, Uzbekistan

⁴Department of Computer Science, Plekhanov Russian University of Economics, Moscow, 117997, Russia

⁵Department of Management, Kuban State Agrarian University named after I.T. Trubilin, Krasnodar, 350044, Russia

⁶Kursk Branch, Financial University under the Government of the Russian Federation, Moscow, 125167, Russia

Emails: gurbanov.p.a@mail.ru; matkarimov_mansur1@mamunedu.uz; sapayeva.n.k@mail.ru; aem735@mail.ru; kulik.andrey.a@mail.ru; ovzanina@fa.ru

Abstract

Maintainable financial fraud detection includes the usage of viable and decent performs in the recognition of fraudulent actions in financial region. A credit card is susceptible to cyber threats, which leads to a fraud of credit card. The fraudster does dishonest action by attaining illegal access to credit card information and this action affects an economic loss for the user as well as company. At present, deep learning (DL) and machine learning (ML), systems were deployed in financial fraud detection owing to their features’ ability of making a great device to find out fraudulent dealings. This paper presents a Financial Data Analysis for Financial Management Based on Cloud Computing Using Deep Reinforcement Learning Model (FDAFM-CCDRLM). The main intention of FDAFM-CCDRLM model is to improve analysis of financial data in the economic management. Initially, the min-max normalization is employed in the data normalization stage to convert a data of input into a suitable format. Besides, the proposed FDAFM-CCDRLM model designs a black‐winged kite algorithm (BKA) for the subset of feature selection process. For the classification process, the double deep Q‐network (DDQN) algorithm has been executed. At last, the artificial bee colony (ABC) algorithm-based hyperparameter range method is done for improving the classification outcomes of the DDQN model. The experimental evaluation of the FDAFM-CCDRLM system can be tested on a benchmark database. The extensive outcomes highlight the significant solution of the FDAFM-CCDRLM approach to the financial data analysis classification process

Keywords: Financial Data Analysis; Min-Max Normalization; Financial Management; Cloud Computing; Deep Reinforcement Learning