Business Administration, Shanghai Lixin University of Accounting and Finance, Shanghai 201209, China.
Comput Intell Neurosci. 2022 Mar 9;2022:6046957. doi: 10.1155/2022/6046957. eCollection 2022.
With the widespread application of IoT technology in the world, the new industry of IoT finance has emerged. Under this new business model, commercial banks and other financial institutions can realize safer and more convenient financial services such as payment, financing and asset management through the application of IoT technology and communication network technology. In the cloud computing model, the local terminal device of IOT will transmit the collected data to the cloud server through the network, and the cloud server will complete the data operation. Cloud computing model can well solve the problem of poor performance of IoT devices, but with the increasing number of IoT terminal devices and huge number of devices accessing the network, cloud computing model is constrained by network bandwidth and performance bottleneck, which brings a series of problems such as high latency, poor real-time and low security. In this paper, based on the new industry of IoT finance which is developing rapidly, we construct a POT (Peaks Over Threshold) over threshold model to empirically analyze the operational risk of commercial banks by using the risk loss data of commercial banks, and estimate the corresponding ES values by using the control variables method to measure the operational risk of traditional commercial banks and IoT finance respectively, and compare the total ES values of the two. This paper adopts the control variable method to reduce the frequency of each type of loss events of operational risk of commercial banks in China respectively.
随着物联网技术在全球的广泛应用，物联网金融这一新产业应运而生。在这种新的商业模式下，商业银行和其他金融机构可以通过物联网技术和通信网络技术的应用，实现支付、融资和资产管理等更加安全便捷的金融服务。在云计算模型中，物联网的本地终端设备将通过网络将采集到的数据传输到云服务器，由云服务器完成数据运算。云计算模型能够很好地解决物联网设备性能较差的问题，但是随着物联网终端设备数量的增加和海量设备接入网络，云计算模型受到网络带宽和性能瓶颈的制约，带来了延迟高、实时性差、安全性低等一系列问题。本文基于快速发展的物联网金融新产业，通过商业银行风险损失数据，构建 POT（超过阈值的峰）超过阈值模型，对商业银行的操作风险进行实证分析，并采用控制变量法分别估计相应的 ES 值，以衡量传统商业银行和物联网金融的操作风险，并比较两者的总 ES 值。本文采用控制变量法，分别降低了中国商业银行操作风险的每种损失事件的发生频率。
