English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Nowadays small and medium-sized enterprises have become an essential part ofthe national economy. With the increasing number of such enterprises, how toevaluate their credit risk becomes a hot issue. Unlike big enterprises withmassive data to analyze, it is hard to find enough information of smallenterprises to assess their financial status. Limited by the lack of primarydata, how to inference small enterprises' credit risk from secondary data, likeinformation of their upstream, downstream, parent, and subsidiary enterprisesattracts big attention from industry and academy. Targeting on accuratelyevaluating the credit risk of the small and medium-sized enterprise (SME), inthis paper, we exploit the representative power of Information Network onvarious kinds of SME entities and SME relationships to solve the problem. Anovel feature named meta path feature proposed to measure the credit risk,which makes us able to evaluate the financial status of SMEs from variousperspectives. Experiments show that our method is effective to identify SMEswith credit risks.

A Meta Path Based Evaluation Method for Enterprise Credit Risk