Peter Martey Addo
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Peter Martey Addo has a total of 2 co-authors. Here are the top co-authors by number of shared publications:
- Bertrand K. Hassani
Université Paris 1 Panthéon-Sorbonne, Paris, France
1 shared publication Dominique Guegan
Laboratory of Excellence for Financial Regulation (LabEx ReFi), Paris 75011, France
1 shared publication
Latest Publications
- Open Access
Credit Risk Analysis Using Machine and Deep Learning Models
Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises.
Open AccessCredit Risk Analysis Using Machine and Deep Learning Models
Due to the advanced technology associated with Big Data, data availability and computing power, most banks or lending institutions are renewing their business models. Credit risk predictions, monitoring, model reliability and effective loan processing are key to decision-making and transparency. In this work, we build binary classifiers based on machine and deep learning models on real data in predicting loan default probability. The top 10 important features from these models are selected and then used in the modeling process to test the stability of binary classifiers by comparing their performance on separate data. We observe that the tree-based models are more stable than the models based on multilayer artificial neural networks. This opens several questions relative to the intensive use of deep learning systems in enterprises.