Abstract
The purpose is to effectively manage the financial market, comprehensive assess personal credit, reduce the risk of financial enterprises. Given the systemic risk problem caused by the lack of credit scoring in the existing financial market, a credit scoring model is put forward based on the deep learning network. The proposed model uses RNN (Recurrent Neural Network) and BRNN (Bidirectional Recurrent Neural Network) to avoid the limitations of shallow models. Afterward, to optimize path analysis, bionic optimization algorithms are introduced, and an integrated deep learning model is proposed. Finally, a financial credit risk management system using the integrated deep learning model is proposed. The probability of default or overdue customers is predicted through verification on three real credit data sets, thus realizing the credit risk management for credit customers.
Highlights
The collapse of the Bretton Woods system has created a lasting influence on international financial markets that have become extremely volatile under accelerating economic globalization (Nikulin & Pekhterev, 2021)
Most existing credit scoring models are implemented with shallow structures, so deep learning is innovatively introduced into the credit scoring model, and the RNN model and Bidirectional Recurrent Neural Network (BRNN) are used for credit scoring
The BRNN model is optimized by the bionic Particle Swarm Optimization (PSO) algorithm and the AdaBound algorithm
Summary
The collapse of the Bretton Woods system has created a lasting influence on international financial markets that have become extremely volatile under accelerating economic globalization (Nikulin & Pekhterev, 2021). In such a financial environment, enterprises, financial institutions, and individual investors might have to bear various unprecedented risks (Nosan, 2019), which seriously harms the healthy development of the national economy and global financial markets (Olivier & Lieven, 2019). Enterprises, financial institutions, and individual investors utilize effective risk prediction models to analyze financial data and avert risks (Ouyang et al, 2021). The study of the financial market risk management system plays an important role in ensuring the stability of the national financial market
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