Evaluation of a Fintech Sales Synthetic Data Generation Model Using a Generative Adversarial Network

The need for more and better information for decision making is fundamental in modern organizations, especially in the financial industry. One type of this information is time series, which allow prediction and estimation of different scenarios, but are difficult to obtain for small and medium sized enterprises (SMEs). This research presents the design and validation of a generative adversarial network (GAN) capable of generating synthetic data for daily sales of Chilean SME. The problem that needs to be resolved is the lack of this kind of data within a Chilean fintech company called Dank. This data can be useful in developing an automatic risk evaluation model and, therefore, in reducing business process time, since risk evaluation is currently being carried out by people. The solution allows maintaining the anonymity of the data and using GAN to obtain different synthetic time series, increasing the data by 10%. It uses images from a vector of random numbers that are in temporal coherence and equal distribution. This research allows SMEs to obtain a greater amount of data, with a simple solution, to make better decisions.