Risk Measurement of the Financial Credit Industry Driven by Data

Guizhi Li · Xuebiao Wang · Datian Bi · Jiayu Hou

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Financial Distress and Bankruptcy Prediction Credit Risk and Financial Regulations Evaluation and Optimization Models

Abstract

The risk measurement of financial credit industry is an important research issue in the field of financial risk assessment. The design of financial credit risk measurement algorithm can help investors avoid greater risks and obtain higher returns, so as to promote the benign development of financial credit industry. Based on the combined deep learning algorithm, this paper studies the risk measurement of financial and credit industry, and proposes a fusion algorithm of deep auto-encoder (DAE) and Long Short-Term Memory (LSTM) network. The algorithm recombines the value of fixed features by using the unsupervised mechanism of DAE, and extracts non fixed features for measurement combined with the memory characteristics of LSTM network. The experimental results show that: compared with single generalized regression neural network and LSTM network, the average accuracy of DAE-LSTM algorithm is improved by about 6.49% and 3.25% respectively, which has a better application effect in credit risk measurement.

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References

2017A deep learning framework for financial time series using stacked autoencoders and long-short term memory 2000Neural network credit scoring models 2016Deep Learning for Finance: Deep Portfolios 2015Risk assessment in social lending via random forests 2012Herding behavior in online P2P lending: An empirical investigation 2021Deep learning for credit scoring: Do or don’t?2015Forecasting exchange rate using deep belief networks and conjugate gradient method 2019Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction

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