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

Credit risk modeling has permeated our everyday life. Most banks andfinancial companies use this technique to model their clients' trustworthiness.While machine learning is increasingly used in this field, the resultinglarge-scale collection of user private information has reinvigorated theprivacy debate, considering dozens of data breach incidents every year causedby unauthorized hackers, and (potentially even more) information misuse/abuseby authorized parties. To address those critical concerns, this paper proposesa framework of Privacy-preserving Credit risk modeling based on AdversarialLearning (PCAL). PCAL aims to mask the private information inside the originaldataset, while maintaining the important utility information for the targetprediction task performance, by (iteratively) weighing between a privacy-riskloss and a utility-oriented loss. PCAL is compared against off-the-shelfoptions in terms of both utility and privacy protection. Results indicate thatPCAL can learn an effective, privacy-free representation from user data,providing a solid foundation towards privacy-preserving machine learning forcredit risk analysis.

PCAL: A Privacy-preserving Intelligent Credit Risk Modeling Framework Based on Adversarial Learning