Modelo de evaluación crediticio basado en principios de un ecosistema digital bancario mediante estrategias de business intelligence y mecanismos de reputación en el sector de industria textil de las pymes colombianas

Abstract

The high degree of competitiveness of the current market and its constant changes requires quick, accurate, and correct decisions for success within global and regional trade. Added to this is the current global situation caused by the COVID-19 virus pandemic, which has had an impact on all markets and financial sectors. For South America, a 5.2% drop in the economy is expected. Some countries in this subregion were greatly affected by the drop in activity in China, which is an important market for its exports of goods (ECLAC, 2020). SMEs are a group of companies within a specific classification. In the Colombian economy, the main characteristic that defines the classification depends on the number of employees available to the organization. The classification is defined as micro, small, and medium-sized companies. These companies represent the fundamental sector of the economy; 99.6% of companies in Colombia are micro, small, and medium-sized companies. World governments, specifically the Colombian government, have opened aid programs for the SME sector, supporting decisions in banking financial entities. The fiscal packages announced in the region are the first response to the socioeconomic impact of the pandemic. For their part, the central banks of the region have announced “unconventional measures” to expand liquidity, including the purchase of public and private assets. (ECLAC, 2020). Current conditions pose a scenario where banking organizations will have to adapt and employ new alternatives to meet the high flow of potential clients, taking into account the implementation and understanding of new technologies and digital ecosystems. Considering the previous scenario, there is an interest in the financial and academic fields to propose new methodologies for defining access to credit services. Evaluating traditional risk analysis models reveals an opportunity to implement a complementary model that allows determining, based on public opinion and ratings collected and published on digital platforms, whether an organization has a favorable profile to consider the disbursement of financial aid. This analysis is carried out by collecting and classifying data from social digital platforms, such as Facebook and Twitter, implementing methodologies based on sentiment analysis where a result is obtained in the form of classification between a positive or negative profile depending on the interpretation of the base text, with this result a model based on Business Intelligence is defined focused on decision-making for the identification of services and products within a digital banking ecosystem. Finally, this model is validated with respect to traditional risk analysis models to obtain the percentage difference between confirmed and denied products for SMEs.