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43 Citations
- Luis Eduardo Boiko FerreiraJ. P. BarddalHeitor Murilo GomesF. Enembreck
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2017 IEEE 29th International Conference on Tools…
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Using data from a leading European P2P platform, machine learning algorithms are applied to build classification models that can predict the success of secondary market offers and it is found that random forests offer the best classification performance.
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The loan selection process in P2P lending is treated as a portfolio optimization problem, with the aim being to select a set of loans that provide a required return while minimizing risk, and using internal rate of return as the measure of return.
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ABSTRACT Effective assessment of borrower credit risk is the greatest challenge for peer-to-peer (P2P) lenders, especially in the Chinese market, where borrowers lack widely recognized credit scores.…
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A two-stage framework that incorporates the credit information into a profit scoring modeling that could identify more profitable loans and thereby provide better investment guidance to the investors compared to the existing one-stage profit scoring alone approach is proposed.
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It is found that the abnormal return tends to trigger default risk significantly, but the default risk can be minimized if a platform has positive recommendations from customers and more transparent information disclosure or is affiliated as the member of the National Internet Finance Association of China.
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This paper addresses the borrower's default prediction problem in the P2P financial ecosystem by using Logistic Regression coupled with Weight of Evidence encoding, and compares the results of the chosen LR approach against two other popular Machine Learning techniques: the k Nearest Neighbors (k-NN) and the Random Forest.
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The results in this research show that J48 and Naïve Bayes are both good in predicting the default in P2P lending sector.
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This paper proposes a holistic data processing flow for the loan status classification of marketplace lending multivariate time series data by using the Bidirectional Long Short-Term Memory model (BiLSTM) to predict “non-default,’ “distressed,” and “default” loan status, which outperforms conventional techniques.
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This paper will use Machine Learning algorithms to classify and optimize peer lending risk and use this data to improve the quality of loans and reduce the likelihood of a borrower default.
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