Title: An intelligent financial management system for optimal resource allocation in an organisation

Authors: Juan Camilo Vanegas Pinto; Jose Aguilar; Elizabeth Suescún; José Alejandro Román; Alejandra Cardenas Montoya; Mateo Florez Restrepo; Laura Sánchez Córdoba

Addresses: Departamento de Finanzas, Universidad EAFIT, Medellín, Colombia ' GIDITIC, Universidad EAFIT, Medellín, Colombia; CEMISID, Faculta de Ingeniería, Universidad de Los Andes, Mérida, Venezuela; Departamento de Automática, Universidad de Alcalá, Alcalá de Henares, Spain ' GIDITIC, Universidad EAFIT, Medellín, Colombia ' Departamento de Ingeniería de Sistemas, Universidad EAFIT, Medellín, Colombia ' Departamento de Ingeniería de Sistemas, Universidad EAFIT, Medellín, Colombia ' Departamento de Ingeniería de Sistemas, Universidad EAFIT, Medellín, Colombia ' Departamento de Ingeniería de Sistemas, Universidad EAFIT, Medellín, Colombia

Abstract: Today, companies require many decision-making tools to maximise their efficiency. This task can be facilitated by the possibility of developing intelligent decision-making systems based on data. In this way, the objective of this paper is to develop a tool to optimise free cash flow that assists in making decisions concerning the allocation of financial resources, so that the profitability can be improved without disproportionately increasing risk. This paper defines a smart system to study the financial performance of a company, and additionally, it prescribes changes in the approach to the allocation of financial resources that management may adopt. The smart system is based on an autonomous cycle of data analysis tasks, which consists of an optimisation process to define the financial resource allocation changes needed towards the accomplishment of the best risk-reward possible that uses a prescriptive model to evaluate the expected behaviour of the company's future free cash flows.

Keywords: data analytics; machine learning; financial forecasting; cash flow forecasting; financial analysis; risk asymmetry analysis.

DOI: 10.1504/IJADS.2023.133145

International Journal of Applied Decision Sciences, 2023 Vol.16 No.5, pp.565 - 586

Received: 16 Dec 2021
Accepted: 21 Apr 2022
Published online: 01 Sep 2023 *

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