The Approach of Machine Learning to Optimize the Bank-Customer Interaction at Pandemic Epochs

Abstract

Along the pandemic created by the Corona virus 2019 (Covid-19 in shorthand), the global economy was observed to experience various turbulent months that were reflected by the increasing of unemployment and the apparition of a procrastinator behavior in all those customers that received a loan at the months before the beginning of pandemic. Because the apparition of pandemic was totally random, it had effects on the micro-economy that in most cases have turned out on the cuts of salaries. From a basic modeling of loan and Gaussian approach, the criteria of Mitchell are employed. The resulting simulations have yielded that up to a 50% of loaned volume of cash would be recovery. It was found that entropic situations would be in part a cause for the deficient management of loans in epochs of pandemic and crisis.