The train data does not represent the ranges of cases that the network will encounter in practice. The network has a several hundred inputs. The main discriminating factors are not present in the available data. E.g. trying to assess the loan application without having knowledge of the applicant's salaries. The network is required to implement a very complex function.
Data collection is a major part of a neural network solution.
Neural networks are data driven models. The quality of the neural network will therefore critically depend on the quality and quantity of the training data. Neural networks are great for data-fusion i.e. combining different types of data from different sources. For example the state of a motor could be determined by a network trained on measurements of sound, temperature, vibrations and flow rate of the lubricant. For prediction and classification applications, require target data and supervised learning.