Risk Modelling and Prediction of Financial Management in Macro Industries using CNN Based Learning

S. C. Sekhar, Sai Bhaskar Reddy Kovvuri, Kanuparthi S R M S Sai Vyshnavi, Sahithi Uppalapati, Kondepu Yaswanth, Rama Krishna Teja
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Abstract

The financial risk management has been around for the past 20% years, it has already grown into a significant field of study. Being familiar with the stock market is not sufficient preparation for a career in risk management in today competitive environment. There are additional responsibilities that come into play here. Understanding the sophisticated mathematical models that are used to price financial derivatives is necessary for model validation, which has grown into its own statistical specialty. In this paper, the risk modelling is conducted using prediction-based model that uses convolutional neural network (CNN) to predict and model the risk in financial systems. Several risk factors associated with the payment gateway is analysed and predicted, based on which the risk is modelled. The simulation shows higher prediction accuracy by the system than the conventional risk models..
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基于CNN学习的宏观行业财务管理风险建模与预测
财务风险管理已经出现了近20年,已经发展成为一个重要的研究领域。在当今竞争激烈的环境中,熟悉股票市场并不能为从事风险管理工作做好充分的准备。这里还有一些额外的责任。了解用于金融衍生品定价的复杂数学模型对于模型验证是必要的,模型验证已经发展成为自己的统计专业。本文采用基于预测的模型进行风险建模,该模型利用卷积神经网络(CNN)对金融系统中的风险进行预测和建模。与支付网关相关的几个风险因素进行了分析和预测,并在此基础上建立了风险模型。仿真结果表明,该系统的预测精度高于传统的风险模型。
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