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Credit scoring models have been widely used in traditional financial institutions for many years. Using these models in P2P Lending have limitations. First, the credit data of P2P usually contains dense numerical features and sparse categorical features. Second, the existing credit scoring models are generally cannot be updated online. The loan transaction of P2P lending is very frequent and the new data leads data distribution to change. A credit scoring model without considering data update causes a serious deviation or even failure in subsequent credit assessment. In this paper, we propose a new online integrated credit scoring model (OICSM) for P2P Lending. OICSM integrates gradient boosting decision tree and neural network to make the credit scoring model can handle two types of features more effectively and update online. Offline and online experiments based on real and representative credit datasets are conducted to verify the effectiveness and superiority of the proposed model. Experimental results demonstrate that OICSM can significantly improve performance due to its advantage in deep learning over two features, and it can further correct model deterioration due to its online dynamic update capability.