Add to ORKGIn this thesis, peer-to-peer lending is explored and analyzed with the objective of fitting a model to accurately predict if borrowers default on their loans or not. The foundation for the thesis is a dataset from LendingClub, a peer-to-peer lending platform based in San Francisco, USA. Detailed information of borrowers’ financial history, personal characteristics and the specifics of each loan is used to predict the probability of default for the various loans in the portfolio. Methods used include elastic net regularization of logistic regression, boosting of decision trees, and bagging with random forests. The results are compared using accuracy metrics and a profitability measure, before a final model selection is carried out
Logistic regression (LR) and support vector machine algorithms, together with linear and nonlinear d...
Under the direction of Dr. Giancarlo Schrementi Predicting loan default is an important problem for ...
Peer to Peer lending has the capacity to transforming the mass banking industry worldwide but credit...
In this thesis, peer-to-peer lending is explored and analyzed with the objective of fitting a model ...
In the United States, stagnant interest rates, bank skepticism after the financial crisis and the ri...
Small and medium enterprises face the challenge of obtaining start-up fund due to the strict rules ...
Successful identification of the potential contributors that might lead to the outcome of loan statu...
Small and medium enterprises face the challenge of obtaining start-up fund due to the strict rules a...
One of the major challenges facing the retail finance market including banks is the issue of credit ...
The thesis presents three empirical chapters on the credit risk and industry potential of the Peer-t...
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and ...
In this paper we explore how predictive modelling can be applied in loan default prediction. The iss...
In the peer to peer (P2P) lending platform, investors hope to maximize their return while minimizing...
We study the credit scoring model in a peer-to-peer (P2P) lending platform which is the core compete...
Logistic regression (LR) and support vector machine algorithms, together with linear and nonlinear d...
Logistic regression (LR) and support vector machine algorithms, together with linear and nonlinear d...
Under the direction of Dr. Giancarlo Schrementi Predicting loan default is an important problem for ...
Peer to Peer lending has the capacity to transforming the mass banking industry worldwide but credit...
In this thesis, peer-to-peer lending is explored and analyzed with the objective of fitting a model ...
In the United States, stagnant interest rates, bank skepticism after the financial crisis and the ri...
Small and medium enterprises face the challenge of obtaining start-up fund due to the strict rules ...
Successful identification of the potential contributors that might lead to the outcome of loan statu...
Small and medium enterprises face the challenge of obtaining start-up fund due to the strict rules a...
One of the major challenges facing the retail finance market including banks is the issue of credit ...
The thesis presents three empirical chapters on the credit risk and industry potential of the Peer-t...
Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and ...
In this paper we explore how predictive modelling can be applied in loan default prediction. The iss...
In the peer to peer (P2P) lending platform, investors hope to maximize their return while minimizing...
We study the credit scoring model in a peer-to-peer (P2P) lending platform which is the core compete...
Logistic regression (LR) and support vector machine algorithms, together with linear and nonlinear d...
Logistic regression (LR) and support vector machine algorithms, together with linear and nonlinear d...
Under the direction of Dr. Giancarlo Schrementi Predicting loan default is an important problem for ...
Peer to Peer lending has the capacity to transforming the mass banking industry worldwide but credit...