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This paper adopts the deep residual network (ResNet 50) to help the processing of complex features of financial data to replace the traditional data processing ...
The deep residual network (ResNet 50) is adopted to help the processing of complex features of financial data to replace the traditional data processing ...
The main work of this paper is to review the predecessors' work of deep learning for financial risk prediction according to three prominent characteristics of ...
May 10, 2022 · Its purpose is to design a financial risk control model based on a deep learning NNs, thereby reducing financial risk.
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This paper tries to establish a more comprehensive and practical financial risk control model by combining the previous research of scholars, the business ...
This work proposes Graph Neural Networks (GNNs) for systemic risk analysis. GNNs use the network structure and feature information to deal with large-scale ...
Abstract—In the realm of financial markets, the precise prediction of option prices remains a cornerstone for effective portfolio management, risk mitigation, ...
The main benefit of a very deep network is that it can represent very complex functions. · However, using a deeper network doesn't always help.
Mar 10, 2022 · In this paper, an AHP neural network model combining subjective and objective methods is proposed. This method can not only overcome the defects ...
It aims to give practical advice for establishing a risk-based governance and testing framework for the mentioned model types.