Credit Risk Modeling Using Non-traditional Data: The Experience of Ping An OneConnect Bank

Name: Credit Risk Modeling Using Non-traditional Data: The Experience of Ping An OneConnect Bank (English version)
SKU: HK1317

Lin, Chen; Zou, Hong; Zheng, Yanfeng; Wan, Tsun-kan

Product #:HK1317

Supplier:The University of Hong Kong

Discipline:Accounting

Setting:Hong Kong

Details:Case (PDF) • 13 pages • 2021-10-31

Subjects:

Risk management Market positioning Big data Strategy Market research Analytics and data science Finance and investing Accounting

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Ping An OneConnect Bank (Hong Kong) Limited is a virtual bank that has pioneered the use of Fintech to provide efficient banking services to both SMEs and retail customers. The bank uses innovative credit risk and loan pricing models that rely on big data analytics, including detailed customs data for its small and medium-sized enterprise (SME) customers in the import/export business.

Learning Objectives

The case allows students to: 1. Evaluate the role of Fintech in enhancing financial inclusion, the relations between traditional banking and Fintech start-ups, and the risk and challenges that Fintech start-ups create for traditional banking 2. Explain the advantages and limitations of a traditional credit risk model, compared to an alternative credit risk model enabled by big data analytics 3. Comprehend Hong Kong's regulatory Sandbox process and the competitive edge given by the Sandbox in the context of Fintech development