19 research outputs found
Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment
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
PHom-GeM: Persistent Homology for Generative Models
Generative neural network models, including Generative Adversarial Network
(GAN) and Auto-Encoders (AE), are among the most popular neural network models
to generate adversarial data. The GAN model is composed of a generator that
produces synthetic data and of a discriminator that discriminates between the
generator's output and the true data. AE consist of an encoder which maps the
model distribution to a latent manifold and of a decoder which maps the latent
manifold to a reconstructed distribution. However, generative models are known
to provoke chaotically scattered reconstructed distribution during their
training, and consequently, incomplete generated adversarial distributions.
Current distance measures fail to address this problem because they are not
able to acknowledge the shape of the data manifold, i.e. its topological
features, and the scale at which the manifold should be analyzed. We propose
Persistent Homology for Generative Models, PHom-GeM, a new methodology to
assess and measure the distribution of a generative model. PHom-GeM minimizes
an objective function between the true and the reconstructed distributions and
uses persistent homology, the study of the topological features of a space at
different spatial resolutions, to compare the nature of the true and the
generated distributions. Our experiments underline the potential of persistent
homology for Wasserstein GAN in comparison to Wasserstein AE and Variational
AE. The experiments are conducted on a real-world data set particularly
challenging for traditional distance measures and generative neural network
models. PHom-GeM is the first methodology to propose a topological distance
measure, the bottleneck distance, for generative models used to compare
adversarial samples in the context of credit card transactions
MQLV: Optimal Policy of Money Management in Retail Banking with Q-Learning
Reinforcement learning has become one of the best approach to train a
computer game emulator capable of human level performance. In a reinforcement
learning approach, an optimal value function is learned across a set of
actions, or decisions, that leads to a set of states giving different rewards,
with the objective to maximize the overall reward. A policy assigns to each
state-action pairs an expected return. We call an optimal policy a policy for
which the value function is optimal. QLBS, Q-Learner in the
Black-Scholes(-Merton) Worlds, applies the reinforcement learning concepts, and
noticeably, the popular Q-learning algorithm, to the financial stochastic model
of Black, Scholes and Merton. It is, however, specifically optimized for the
geometric Brownian motion and the vanilla options. Its range of application is,
therefore, limited to vanilla option pricing within financial markets. We
propose MQLV, Modified Q-Learner for the Vasicek model, a new reinforcement
learning approach that determines the optimal policy of money management based
on the aggregated financial transactions of the clients. It unlocks new
frontiers to establish personalized credit card limits or to fulfill bank loan
applications, targeting the retail banking industry. MQLV extends the
simulation to mean reverting stochastic diffusion processes and it uses a
digital function, a Heaviside step function expressed in its discrete form, to
estimate the probability of a future event such as a payment default. In our
experiments, we first show the similarities between a set of historical
financial transactions and Vasicek generated transactions and, then, we
underline the potential of MQLV on generated Monte Carlo simulations. Finally,
MQLV is the first Q-learning Vasicek-based methodology addressing transparent
decision making processes in retail banking
Visualization of AE's Training on Credit Card Transactions with Persistent Homology
Auto-encoders are among the most popular neural network architecture for
dimension reduction. They are composed of two parts: the encoder which maps the
model distribution to a latent manifold and the decoder which maps the latent
manifold to a reconstructed distribution. However, auto-encoders are known to
provoke chaotically scattered data distribution in the latent manifold
resulting in an incomplete reconstructed distribution. Current distance
measures fail to detect this problem because they are not able to acknowledge
the shape of the data manifolds, i.e. their topological features, and the scale
at which the manifolds should be analyzed. We propose Persistent Homology for
Wasserstein Auto-Encoders, called PHom-WAE, a new methodology to assess and
measure the data distribution of a generative model. PHom-WAE minimizes the
Wasserstein distance between the true distribution and the reconstructed
distribution and uses persistent homology, the study of the topological
features of a space at different spatial resolutions, to compare the nature of
the latent manifold and the reconstructed distribution. Our experiments
underline the potential of persistent homology for Wasserstein Auto-Encoders in
comparison to Variational Auto-Encoders, another type of generative model. The
experiments are conducted on a real-world data set particularly challenging for
traditional distance measures and auto-encoders. PHom-WAE is the first
methodology to propose a topological distance measure, the bottleneck distance,
for Wasserstein Auto-Encoders used to compare decoded samples of high quality
in the context of credit card transactions.Comment: arXiv admin note: substantial text overlap with arXiv:1905.0989