大数据信用评价的进展与挑战

Progress and Challenges of Credit Evaluation for Big Data

10.6338/JDA.201612_11(6).0001

张波(Bo Zhang)；杨翰方(Han Fang Yang)；王汉生(Han Sheng Wang)

Journal of Data Analysis

11卷6期（2016 / 12 / 01）

1 - 12

簡體中文

信用评价一直是商业、管理活动的重要问题。随着数据可获得性的提高，新的信用需求催生出大数据信用评价的兴起，带动了产业的更替与发展，组织的裂变与融合，及技术继承与创新。在此国内外的研究成果丰富的理论支撑基础上，完善和拓展微观信用评价理论与相关方法成为学术研究的热点。基于个人、中小企业在社会经济活动对征信供给的要求，我们认为大数据信用评价现阶段存在着四大挑战：基于大数据技术改变现有信用评价模式；构建大数据环境下信用指标体系；针对信用数据的特征建立结构化与非结构化数据模型；整合多种数据来源构建信用模型。

Credit evaluation has always been an important issue in business and management activities. With the improvement of data availability, the new credit demand has spawned the rise of large data credit evaluation, not only led to the replacement and development of the industry, but also led to the organization of fission and integration and technology inheritance and innovation. On the basis of the rich theoretical support at home and abroad, perfecting and expanding the micro credit evaluation theory and related methods have become the focus of academic research. Based on the request of individual and small and small and medium enterprises in the credit supply of social economic activities, we believe that there are four challenges in the process of large data credit evaluation: changing the existing credit evaluation model based on big data technology; constructing credit index system under big data environment; constructing structured and unstructured data models for the characteristics of credit data; constructing a credit model by consolidating multiple data sources.

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