建立信用风险模型的可行性分析

Paweł Marcinkowski
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引用次数: 0

摘要

FinAi已经承担了一个项目,重点是利用替代数据源开发创新的信用风险模型,例如来自社交媒体平台(Facebook、LinkedIn)和手机记录的数据。根据FinAi S.A.和总部位于华沙的国家研究与发展中心(NCBiR)之间的资助协议,该项目由欧洲区域发展基金共同资助。项目的初始阶段包括收集数据并利用这些数据构建客户关系网络图。此外,利用外部数据和FinAi管理的数据,制定了具体指标。在专家的监督下,使用这些指标来训练包含图结构的预测模型。与行业内常用的传统模型相比,构建的模型具有更高的预测能力。本研究探讨了基于获得的数据创建信用风险模型的可行性,并评估了来自替代来源的数据的质量。事实证明,替代数据源只适用于一小部分人口,而且其质量已被证明不令人满意。事实证明,数据集的规模不足以建立稳健的信用风险模型,也不足以在银行机构内使用的模型中获得竞争优势。
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Analiza dotycząca możliwości budowy modelu ryzyka kredytowego
FinAi has undertaken a project focused on the development of an innovative credit risk model utilizing alternative data sources, such as data from social media platforms (Facebook, LinkedIn) and mobile phone records. This project was co-financed through the European Regional Development Fund under a funding agreement between FinAi S.A. and the National Centre for Research and Development (NCBiR), headquartered in Warsaw. The initial phase of the project involved the collection of data and their utilization in constructing a network graph of customer relationships. Furthermore, leveraging external data as well as data managed by FinAi, specific indicators were formulated. These indicators were employed under the supervision of experts to train a predictive model that incorporated graph structures. The model thus constructed was to exhibit a higher predictive capability compared to conventional models commonly employed within the industry. The study explored the feasibility of creating a credit risk model based on the acquired data and assessed the quality of data originating from alternative sources. It was demonstrated that alternative data sources were populated for a small fraction of the population, and their quality has proven unsatisfactory. The scale of the dataset proved inadequate for establishing a robust credit risk model or attaining a competitive advantage over the models in use within banking institutions.
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