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5. Conclusions and Future Works
5.1 Conclusions
This research derives a hybrid ANN mechanism for timely and effectively predicting the credit score about students who try to borrow money on P2P Lending platform. Implemented the experiment with recently released machine learning tool-TensorFlow with GPU device. Using the real data collected from the P2P Lending platform to train the ANN and found some valuable issues based on the experimental results:
1. Issue on ANN algorithm field. Although we obtain the ANN algorithm which has already designed and it can do the perfect prediction in training data, it still needs a lot of great effort to apply to the real-world situation. Especially, the giving of credit score are very sensitive in P2P students lending field, so it is important that we still need to improve the ANN model.
2. In the application field, the most important task in risk control is to be able to identify the Poor group. The experiment results show that it can predict both Excellent and Moderate group with an accuracy rate of 65% or more. However, it can only predict Poor group with an accuracy rate of 54%.
3. Continuing the second point, since the experiment was in the initial stage, the training data was not enough for us to train the ideal ANN model. Adding more training data in the future is believed to improve forecast accuracy. Due to the ANN can self-adjust the parameters and the proposed algorithm will automatically increase or decrease the number of hidden nodes. It is indeed an auxiliary method for the credit scoring system without certain rules.
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5.2 Future works
In the research process, we face different issues, difficulties and experiences in all aspects. Here are the future outlook and recommendations for this study:
1. The experiment results show that using a machine learning tool TensorFlow with graphics processing GPU operations under a large amount of input data, input variables and complex algorithms are indeed significant for ANN. The training time of ANN can be in hours. If we can add the comparisons about performance and experiment time with other method, it will be more representative of whether ANN is suitable for predicting credit score in students P2P Lending.
2. As the training time is extended, the established TensorFlow graph will increase, and the system will not release the memory, resulting in slow operation. From the empirical results, it is found that GPUs do have good computing performance. If we upgrade hardware devices in the future and use multiple GPUs to perform neural network operations, how much time can be reduced will be a research issue.
3. Continuing the second point, if it is possible to reduce training time due to hardware upgrades, consider using more data as a training paradigm. The training paradigm for this study is 70% of the data and it is recommended to use 80% of the data, even all the data are trained as training examples in the future.
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REFERENCE
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Chinese Reference
1. 李坤霖，(2017)，應用倒傳遞類神經網路於 P2P 借貸投資報酬率預測之研究
——以Lending Club 為例，資訊管理學系碩士論文