The accounting information system enables the presentation of data regarding both the past and the future of an organization through financial reporting and annual budgeting, respectively. In this context, the use of econometric models and computational methods has been frequently employed to develop bankruptcy prediction models, allowing stakeholders to make decision in time. A digital accounting facilitates the necessary data. In addition, digital accounting models enable decision-makers to monitor risk and achieve better outcomes in organizational assessment. Given this, the objective of this research is to discuss the effects of digital accounting in extracting financial information from unstructured sources (e.g., financial reports and accounts in PDF format) and integrating them into databases (e.g., Excel), thereby enhancing the robustness of the data that will subsequently be used in problem solving, such as bankruptcy prediction analyses, compared to traditional models. The methodology developed in this research includes a theoretical analysis through a literature review and an empirical analysis involving experiments with different tools applied to a case study. The results highlight the effects of the evolution of digital accounting use, particularly in extraction strategies with their limitations and their potential of using Business Intelligence tools and predictive models for corporate bankruptcy. These advancements support the utility of transition towards digital accounting practices, allowing for the consideration of a greater volume of information, including qualitative information, that facilitate data transfer procedures through artificial intelligence and freeing accountants and financial analysts for value-added activities in problems as bankruptcy forecasting.