TY - GEN
T1 - Modelos preditivos de insolvências
T2 - 18th Iberian Conference on Information Systems and Technologies, CISTI 2023
AU - Ildefonso, Mariana V.S.
AU - Laureano, Raul M.S.
AU - Vasarhelyi, Miklos A.
N1 - Publisher Copyright: © 2023 ITMA.
PY - 2023
Y1 - 2023
N2 - This study aims to explore the importance of predicting financial distress, insolvency and bankruptcy for investors, creditors, banks and other stakeholders of companies due to the likelihood of corporate default. Since the 2008 financial crisis, it has been a priority for firms to find the best predictive model for forecasting possible weak conditions. This study presents a systematic review of work already done and increases the degree of knowledge through the use of advanced data analysis techniques and using financial and non-financial indicators.
AB - This study aims to explore the importance of predicting financial distress, insolvency and bankruptcy for investors, creditors, banks and other stakeholders of companies due to the likelihood of corporate default. Since the 2008 financial crisis, it has been a priority for firms to find the best predictive model for forecasting possible weak conditions. This study presents a systematic review of work already done and increases the degree of knowledge through the use of advanced data analysis techniques and using financial and non-financial indicators.
KW - Data Mining
KW - Insolvency
KW - Machine Learning
KW - Predictive Models
UR - http://www.scopus.com/inward/record.url?scp=85169822995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85169822995&partnerID=8YFLogxK
U2 - 10.23919/CISTI58278.2023.10211516
DO - 10.23919/CISTI58278.2023.10211516
M3 - Conference contribution
T3 - Iberian Conference on Information Systems and Technologies, CISTI
BT - 2023 18th Iberian Conference on Information Systems and Technologies, CISTI 2023
PB - IEEE Computer Society
Y2 - 20 June 2023 through 23 June 2023
ER -