Provide intelligent classification model based on perceptron artificial neural network (MLP) and hierarchical analysis (AHP) in digital marketing services to prioritize liquidity and investment risk

Subject Areas : Journal of Investment Knowledge

Alireza Ashouri Roudposhti ¹ (Department of Business Management, Faculty of Management and Economics, University of Science and Research, Islamic Azad University, Tehran, Iran)
Hormoz Mehrani ² (assistant profesoor)
Karim Hamdi ³ (Associate professor)

Received: 2020-12-16 Accepted : 2021-01-10 Published : 2021-03-21

Keywords: "Perceptron Neural Network (MLP)", "Artificial Intelligence", "AHP", "Digital Marketing Services", "Liquidity Risk",

Abstract :

The present study, using machine learning and polling techniques, attempts to examine the automated strategic model in order to classify and explore the ideas presented about specific services that have been studied in this area in the field of investment. Provide results in digital marketing services. The neural network-based model, by identifying related opinions, measures different characteristics at different levels of evaluation and automatically categorizes opinions depending on the quality of the presentation. Financial crises in the banking system are usually due to the inability to manage financial risks and liquidity, which is a factor in the lack of transparency and ability to manage capital. Thus, the existence of such uncertainties has reduced the interest of investors in industrial and executive partnerships. This article has been established with the aim of identifying the factors affecting liquidity risk and also providing an intelligent model for predicting and classifying liquidity risk factors, identifying and prioritizing the factors involved. For this purpose, the method of intelligent measurement using perceptron neural network (MLP) has been used, which is considered as a practical approach to artificial intelligence. For this purpose, the necessary studies on financial information and liquidity in Bank Mellat branches in Tehran (consisting of 36 branches) have been considered and for the sample population, a random cluster set of 374 selected customers and investors has been used.

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