Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment
This addresses the need for better client action prediction in banking due to digitalization and regulations, but it is incremental as it adapts existing methods to a specific domain.
The paper tackles the problem of predicting sparse client actions in banking by proposing CPOPT-Net, which combines CP tensor decomposition and neural networks, achieving accurate predictions for personalized recommendations.
The digital revolution of the banking system with evolving European regulations have pushed the major banking actors to innovate by a newly use of their clients' digital information. Given highly sparse client activities, we propose CPOPT-Net, an algorithm that combines the CP canonical tensor decomposition, a multidimensional matrix decomposition that factorizes a tensor as the sum of rank-one tensors, and neural networks. CPOPT-Net removes efficiently sparse information with a gradient-based resolution while relying on neural networks for time series predictions. Our experiments show that CPOPT-Net is capable to perform accurate predictions of the clients' actions in the context of personalized recommendation. CPOPT-Net is the first algorithm to use non-linear conjugate gradient tensor resolution with neural networks to propose predictions of financial activities on a public data set.