  In this paper, we describe application of Neuroevolution to a P2P lending
problem in which a credit evaluation model is updated based on streaming data.
We apply the algorithm Neuroevolution of Augmenting Topologies (NEAT) which has
not been widely applied generally in the credit evaluation domain. In addition
to comparing the methodology with other widely applied machine learning
techniques, we develop and evaluate several enhancements to the algorithm which
make it suitable for the particular aspects of online learning that are
relevant in the problem. These include handling unbalanced streaming data, high
computation costs, and maintaining model similarity over time, that is training
the stochastic learning algorithm with new data but minimizing model change
except where there is a clear benefit for model performance
