信息环境下债务人财务责任的前瞻性分析

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Intelligenza Artificiale Pub Date : 2021-01-15 DOI:10.24891/IA.24.1.72
L. Sungatullina, Il'mira R. Badgutdinova
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引用次数: 0

摘要

主题本文在对潜在交易对手的财务偿付能力进行前瞻性评估的基础上,讨论了应收账款管理问题。目标。本文旨在通过确定快速流动性比率和影响因素之间是否存在统计上显著的关系,在检查交易对手的可靠性时,开发应收账款管理的方法。方法。在研究中,我们采用了分析与综合、概括与比较、逻辑与系统推理以及随机分析的方法。后果本文提出了一种基于Gretl软件专用工具的交易对手财务稳健性前瞻性评估方法。该方法是通过在合同阶段提供预期和估计信息来管理组织应收款的一种方式,以最大限度地减少执行协议时的潜在风险。结论。拟议的交易对手信用度前瞻性评估算法将有助于降低不偿还应收款的风险,并制定交易对手获得贷款必须满足的最低要求。研究结果可应用于组织的实际活动,作为应收账款管理活动发展的一部分。
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A prospective analysis of the financial responsibility of debtors in the information environment
Subject. This article deals with the issues of receivables management on the basis of a prospective assessment of the financial solvency of potential counterparties. Objectives. The article aims to develop methodological approaches to the management of receivables when checking the counterparty for reliability by identifying the existence of a statistically significant relationship between the quick liquidity ratio and impact factors. Methods. For the study, we used the methods of analysis and synthesis, generalization and comparison, logical and systemic reasoning, and stochastic analysis. Results. The article proposes a developed methodology of prospective assessment of the financial soundness of counterparties on the basis of special tools of the Gretl software. The methodology is a way to manage an organization's receivables by providing prospective and estimated information at the contract stage to minimize potential risks when executing agreements. Conclusions. The proposed algorithm of prospective assessment of the creditworthiness of counterparties will help reduce the risk of non-repayment of receivables and develop minimum requirements, which the counterparty must meet in order to obtain a loan. The results of the study can be applied in the organization's practical activities as part of the development of receivables management activities.
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
期刊最新文献
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