Application of Risk Management to the Level of Financial Efficiency in BPD JAWA TENGAH

Luthfan, Tito Cayadi; Manda, Gusganda Suria; Sosrowidigdo, Suryanto

doi:10.36778/jesya.v4i1.333

Cited by 2 publications

(2 citation statements)

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“…Therefore, it is necessary to establish a scientific early warning mechanism, and risk measurement provides the basic basis for early warning. The function of asset value evaluation is divided into two types in this system according to the purpose of evaluation, namely, risk of credit value fluctuation type and liquidity risk assessment [11][12].…”

Section: Risk Assessment Management Function 1) Asset Value Risk Assessmentmentioning

confidence: 99%

Bank Financial Business Risk Assistant Management System Based on Clustering Algorithm

Wang¹

2021

Proceedings of the 2021 International Conference on Public Relations and Social Sciences (ICPRSS 2021)

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In recent years, compliance risk has gradually become an important risk faced by China's commercial banks in addition to credit risk, market risk and operational risk. The lifeline of commercial banks is risk management. In view of the harmfulness of the financial crisis, this paper tries to find out the relevant index parameters that have obvious impact on the financial risk. By building a prediction model, it can predict the risk that may have an impact on China, effectively find the potential financial risk, serve the operation of the macro financial market, and improve the accuracy of financial supervision. In this paper, the current web crawler technology and clustering data mining analysis technology are applied to build an efficient audit risk management system based on bank financial risk management. It takes a lot of time, manpower and energy to screen the original messy and high-risk huge basic data, which effectively improves the management and control efficiency of audit risk in the current social security field, it reduces the workload of audit risk identification and control in the audit process, and can be popularized in some areas. Based on the construction of chaotic neural network prediction model based on wavelet clustering algorithm and FCM clustering algorithm, this paper makes an empirical comparison of the early warning effect of the two models, and compares the advantages and disadvantages of adding different clustering algorithms and chaotic neural network prediction through empirical comparison. After model comparison and analysis, it is found that the risk prediction system of chaotic neural network prediction model based on wavelet clustering algorithm has high accuracy, and the maximum error is only 0.1173, which meets the needs of users.

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Section: Risk Assessment Management Function 1) Asset Value Risk Assessmentmentioning

confidence: 99%

Bank Financial Business Risk Assistant Management System Based on Clustering Algorithm

Wang¹

2021

Proceedings of the 2021 International Conference on Public Relations and Social Sciences (ICPRSS 2021)

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“…The results showed that the companies' positioning in the sample changed. Luthfan et al [ 7 ] noted that efficiency was an indicator of bank performance; a bank's efficiency was affected by how management manages risk. The author measured the impact of financing risk, operational risk, and liquidity risk on the level of efficiency.…”

Section: Introductionmentioning

confidence: 99%

Application of Three-Stage DEA Model Combined with BP Neural Network in Microfinancial Efficiency Evaluation

Yang

Mei

et al. 2022

Computational Intelligence and Neuroscience

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The research performed here intends to explore the future development model of new rural financial institutions and determine the financial efficiency goals, thereby providing a huge stage for the development of new rural financial institutions. It applies data envelopment analysis (DEA) to assess financial efficiency to make up for the research gap. First, the relevant theories of rural finance are discussed. Then, some indicators are selected to build an evaluation system. In addition, the DEA method is used to evaluate the rural financial efficiency in Hebei Province by listing the input indexes and output indexes. After training, the backpropagation neural network (BPNN) model is designed and simulated to obtain the evaluation results. The research results show that before 2015, the comprehensive efficiency of Xingtai, Hengshui, Shijiazhuang, and Langfang showed a downward trend. After 2015, the comprehensive efficiency of all cities in Hebei Province tended to be stable, generally stable at about 0.95. It suggests that rural finance in Hebei Province has developed stably in recent years, and the overall efficiency of rural finance has been improved to a certain extent. The simulation results of BPNN demonstrate that the operation efficiency evaluation result of the poverty alleviation development model in the financial support industry is 0.6995, in the interval (0.6, 0.8). In addition, the model operation efficiency is better, indicating that the poverty alleviation development model of the financial support industry has achieved good results and promoted the development of poor rural areas. The research content can provide reference and inspiration for the continuous promotion of rural finance and the formulation and implementation of financial institution reform policies.

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Application of Risk Management to the Level of Financial Efficiency in BPD JAWA TENGAH

Cited by 2 publications

References 0 publications

Bank Financial Business Risk Assistant Management System Based on Clustering Algorithm

Bank Financial Business Risk Assistant Management System Based on Clustering Algorithm

Application of Three-Stage DEA Model Combined with BP Neural Network in Microfinancial Efficiency Evaluation

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