Practical Validation in a Neutrosophic Environment of the NEBS Methodology for the Optimization of SME Financing through Machine Learning

Abstract

Micro and small enterprises (MSEs) have generated great opportunities for the growth of countries in the Latin American region. Unfortunately, as a result of the global crisis caused by the Sar-Cov-2, MSEs were severely affected. The main objective of this investigation is to validate in a practical way in a neutrosophic environment the use of a predictive Machine Learning technique that demonstrates the probability of the return on investment that a candidate investor will obtain with respect to a given business plan. With them it is expected that the investor can make the decision to finance a MSE, with the positive decision will close gaps in the growth of micro and small enterprises in Peru. The research is descriptive and predictive, with a research design of post-test only and control group. Neutrosophic TOPSIS was used as a technique. NEBS turns out to be efficient for the applicability of Machine Learning by obtaining statistical evidence to accept the hypotheses proposed for the finance sector in micro and small en-terprises in Peru. The results showed that the use of Machine Learning is validated, and its implementation increases the amount of financing obtained, decreases the evaluation time of requirements, reduces the number of complaints, and increases the number of formal sources used. Machine Learning research should be continued due to the complexity of this technology, which is con-stantly evolving.…