Investigations on Classification Methods for Loan Application Based on Machine Learning As there is an increasing trend of people consuming by debit in China, financial organizations deal with a lot of loan applications. If customers cannot repay the loans on time, the organizations have to cover the loss. Therefore it is important to predict correctly whether a customer will repay the loan on time. Typical machine learning methods can be employed to exploit customersu0027 financial information and give valuable judgements. We investigated the function of Deep Neural Network (DNN) in this work, as it achieves high successful rate in fields of image recognition, speech recognition and natural language processing. We compared it with traditional learning methods, such as Naive Bayes, decision tree and KNearest Neighbor. Experiments showed that DNN achieves better performance than its traditional competitors. The accuracy and recall of DNN are 0.73 and 0.42 respectively. Its Itscore is 25% higher than the best one of traditional methods.

Blockchain financial investment based on deep learning network algorithm Abstract   In order to study the use of indepth learning to process financial data, it is proposed that the related technology of neural network and deep learning can be applied to financial data, and real stock index and futures data can be used to explore the application effect of neural network and indepth learning. Firstly, the theory and model of indepth learning and neural network are introduced in detail. Secondly, a simple neural network and indepth learning model are used in the stock index and futures price forecast. The data used in the input of the model include the price of a stock in the current trading and some data indicators, and the closing price of a stock in the next time. The decline will be reflected in output. If the output is up for 1 and down for 0, new data will be input after the training of the model. Finally, the output data can be compared with the real data to judge the application effect of the model, after comparing e and analyzing the application effect of the model. The results show that the research in this study can help investors to build an automated investment model and the investment strategy of the stock market. The construction can be used for reference to improve investorsxe2x80x99 investment strategy and return rate.