Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning

Kim, Ji Yoon; Cho, Sung Bae

doi:10.3390/math7111041

Cited by 26 publications

(13 citation statements)

References 37 publications

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“…Wang et al (2018) based on online operation behavior data of borrowers in P2P lending proposed a consumer credit scoring method based on the LSTM model and evaluated the method on a real data set [ 26 ]. Kim et al (2019) proposed a convolutional neural networks (CNNs) architecture for classifying the loan status of borrowers in P2P lending to automatically select complex features and improve model performance [ 27 ]. Pawiak et al (2019) proposed a support vector machine deep genetic cascade ensemble classifier (DGCEC) based on evolutionary computation, ensemble learning, and deep learning technology, which could effectively classify borrowers, accept, or reject applications.…”

Section: Literature Reviewmentioning

confidence: 99%

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

Liu

et al. 2021

Entropy

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Background: the credit scoring model is an effective tool for banks and other financial institutions to distinguish potential default borrowers. The credit scoring model represented by machine learning methods such as deep learning performs well in terms of the accuracy of default discrimination, but the model itself also has many shortcomings such as many hyperparameters and large dependence on big data. There is still a lot of room to improve its interpretability and robustness. Methods: the deep forest or multi-Grained Cascade Forest (gcForest) is a decision tree depth model based on the random forest algorithm. Using multidimensional scanning and cascading processing, gcForest can effectively identify and process high-dimensional feature information. At the same time, gcForest has fewer hyperparameters and has strong robustness. So, this paper constructs a two-stage hybrid default discrimination model based on multiple feature selection methods and gcForest algorithm, and at the same time, it optimizes the parameters for the lowest type II error as the first principle, and the highest AUC and accuracy as the second and third principles. GcForest can not only reflect the advantages of traditional statistical models in terms of interpretability and robustness but also take into account the advantages of deep learning models in terms of accuracy. Results: the validity of the hybrid default discrimination model is verified by three real open credit data sets of Australian, Japanese, and German in the UCI database. Conclusion: the performance of the gcForest is better than the current popular single classifiers such as ANN, and the common ensemble classifiers such as LightGBM, and CNNs in type II error, AUC, and accuracy. Besides, in comparison with other similar research results, the robustness and effectiveness of this model are further verified.

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Section: Literature Reviewmentioning

confidence: 99%

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

Liu

et al. 2021

Entropy

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“…The accuracy of the considered models was between 0.61 and 0.70. The convolutional neural network is used in Kim (2019) for repayment prediction in Peer-to-Peer Social Lending. The 855500 rows and 63 attributes are used (such as loan amount, payment amount, and loan period).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Repayment Prediction in Leasing Companies

Hernes¹,

Kozierkiewicz²,

Maleszka³

et al. 2021

ERSJ

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This paper aims to improve repayment prediction in leasing companies using a deep learning model. Design/Methodology/Approach: In this work, we prepare some deep learning models and compare them with other solutions based on artificial intelligence like, multiple regression, decision tree, random forest, and bagging classifier. Findings: The developed model enables automatic analysis of large amounts of data that changes quickly and is often unstructured. Additionally, the input vectors consist of specific attributes related to leasing. The results of experiments allow us to conclude that the prediction accuracy of the developed model is higher than reference models used currently in leasing companies. Practical Implications: The developed model has recently been implemented in the Decision Engine system (a system used by leasing companies in Poland) developed by BI Technologies Sp. Z o.o. Company. Originality/Value: Financial institutions automate and simplify credit procedures, eliminating the analyst from the process and replacing him with automatic decision-making processes based on a scoring or similar models. However, to automatically analyze the significance of phenomena occurring in the environment of organizations that affect the assessment of customer's repayments, it is necessary to use artificial intelligence tools.

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“…Thus, researchers are still working to find a significant solution in order to choose the right features and advance the artificial intelligence (AI) based classification techniques. Similarly, the background noise in a real-world voice could also be dramatically effective on the machine learning system [1,2]. Nevertheless, the development of a decent speech-based emotion recognition system can easily increase the user experience in different areas with the HCI, such as AI cyber security and mobile health (mHealth) [3].…”

Section: Introductionmentioning

confidence: 99%

CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network

2020

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Artificial intelligence, deep learning, and machine learning are dominant sources to use in order to make a system smarter. Nowadays, the smart speech emotion recognition (SER) system is a basic necessity and an emerging research area of digital audio signal processing. However, SER plays an important role with many applications that are related to human–computer interactions (HCI). The existing state-of-the-art SER system has a quite low prediction performance, which needs improvement in order to make it feasible for the real-time commercial applications. The key reason for the low accuracy and the poor prediction rate is the scarceness of the data and a model configuration, which is the most challenging task to build a robust machine learning technique. In this paper, we addressed the limitations of the existing SER systems and proposed a unique artificial intelligence (AI) based system structure for the SER that utilizes the hierarchical blocks of the convolutional long short-term memory (ConvLSTM) with sequence learning. We designed four blocks of ConvLSTM, which is called the local features learning block (LFLB), in order to extract the local emotional features in a hierarchical correlation. The ConvLSTM layers are adopted for input-to-state and state-to-state transition in order to extract the spatial cues by utilizing the convolution operations. We placed four LFLBs in order to extract the spatiotemporal cues in the hierarchical correlational form speech signals using the residual learning strategy. Furthermore, we utilized a novel sequence learning strategy in order to extract the global information and adaptively adjust the relevant global feature weights according to the correlation of the input features. Finally, we used the center loss function with the softmax loss in order to produce the probability of the classes. The center loss increases the final classification results and ensures an accurate prediction as well as shows a conspicuous role in the whole proposed SER scheme. We tested the proposed system over two standard, interactive emotional dyadic motion capture (IEMOCAP) and ryerson audio visual database of emotional speech and song (RAVDESS) speech corpora, and obtained a 75% and an 80% recognition rate, respectively.

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Towards Repayment Prediction in Peer-to-Peer Social Lending Using Deep Learning

Cited by 26 publications

References 37 publications

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

A Two-Stage Hybrid Default Discriminant Model Based on Deep Forest

Deep Learning for Repayment Prediction in Leasing Companies

CLSTM: Deep Feature-Based Speech Emotion Recognition Using the Hierarchical ConvLSTM Network

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