TY - GEN
T1 - Predictive model for the evaluation of credit risk in banking entities based on machine learning
AU - Haro, Brenda
AU - Ortiz, Cesar
AU - Armas, Jimmy
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - In this paper, we propose a technology model of predictive analysis based on machine learning for the evaluation of credit risk. The model allows predicting the credit risk of a person based on the information held by an institution or non-traditional sources when deciding whether to grant a loan. In this context, the financial situation of borrowers and financial institutions is compromised. The complexity of this problem can be simplified using new technologies such as Machine Learning in a Cloud Computing platform. Azure was used as a tool to validate the technological model of predictive analysis and determine the credit risk of a client. The proposed model used the Two-Class Boosted Decision Tree algorithm that gave us a greater AUC of 93% accuracy, this indicator was taken as having greater repercussion in the proof of concept developed because it is wanted to predict more urgently the number of possible applicants who do not comply with the payment of debits.
AB - In this paper, we propose a technology model of predictive analysis based on machine learning for the evaluation of credit risk. The model allows predicting the credit risk of a person based on the information held by an institution or non-traditional sources when deciding whether to grant a loan. In this context, the financial situation of borrowers and financial institutions is compromised. The complexity of this problem can be simplified using new technologies such as Machine Learning in a Cloud Computing platform. Azure was used as a tool to validate the technological model of predictive analysis and determine the credit risk of a client. The proposed model used the Two-Class Boosted Decision Tree algorithm that gave us a greater AUC of 93% accuracy, this indicator was taken as having greater repercussion in the proof of concept developed because it is wanted to predict more urgently the number of possible applicants who do not comply with the payment of debits.
KW - Cloud azure
KW - Credit risk
KW - Credit risk analysis
KW - Machine learning azure
KW - Machine learning studio
UR - https://www.scopus.com/pages/publications/85068615016
U2 - 10.1007/978-3-030-16053-1_59
DO - 10.1007/978-3-030-16053-1_59
M3 - Contribución a la conferencia
AN - SCOPUS:85068615016
SN - 9783030160524
T3 - Smart Innovation, Systems and Technologies
SP - 605
EP - 612
BT - Proceedings of the 4th Brazilian Technology Symposium (BTSym’18) - Emerging Trends and Challenges in Technology
A2 - Iano, Yuzo
A2 - Loschi, Hermes José
A2 - Arthur, Rangel
A2 - Saotome, Osamu
A2 - Vieira Estrela, Vânia
PB - Springer Science and Business Media Deutschland GmbH
T2 - 4th Brazilian Technology Symposium, BTSym 2018
Y2 - 23 October 2018 through 25 October 2018
ER -