User-Device Authentication in Mobile Banking Using APHEN for Paratuck2 Tensor Decomposition

Abstract

The new financial European regulations such as PSD2 are changing the retail banking services. Noticeably, the monitoring of the personal expenses is now opened to other institutions than retail banks. Nonetheless, the retail banks are looking to leverage the user-device authentication on the mobile banking applications to enhance the personal financial advertisement. To address the profiling of the authentication, we rely on tensor decomposition, a higher dimensional analogue of matrix decomposition. We use Paratuck2, which expresses a tensor as a multiplication of matrices and diagonal tensors, because of the imbalance between the number of users and devices. We highlight why Paratuck2 is more appropriate in this case than the popular CP tensor decomposition, which decomposes a tensor as a sum of rank-one tensors. However, the computation of Paratuck2 is computational intensive. We propose a new APproximate HEssian-based Newton resolution algorithm, APHEN, capable of solving Paratuck2 more accurately and faster than the other popular approaches based on alternating least square or gradient descent. The results of Paratuck2 are used for the predictions of users' authentication with neural networks. We apply our method for the concrete case of targeting clients for financial advertising campaigns based on the authentication events generated by mobile banking applications.

VecHGrad for Solving Accurately Complex Tensor Decomposition

“…The data sets used in machine learning [1] and data mining [2] are multi-dimensional and, therefore, tensors and their decompositions are highly appropriate [3]. Because of the size of modern data sets, Tensor Decompositions (TDs), such as the CP/PARAFAC [4,5] decomposition, later denoted CP, are now challenged by other TDs such as DEDICOM [6] and PARATUCK2 [7,8]. CP decomposes the original tensor as a sum of rank-one tensors, as illustrated in figure 1, whereas DEDICOM and PARATUCK2 decompose the original tensor as a product of matrices and diagonal tensors, as shown in figures 2 and 3, respectively.…”