Micro-, small and medium-sized enterprises (MSMEs) in Hong Kong are key players in the city’s economy, and the primary source of employment. However, they often encounter difficulties borrowing from banks due to Hong Kong’s lack of a credit information infrastructure and the significant burden faced by banks in conducting credit scoring and monitoring related processes. Against this background, the Hong Kong Monetary Authority (HKMA) engaged the Hong Kong Applied Science and Technology Research Institute (ASTRI) to explore how new financial technologies could be used to develop an alternative credit scoring framework for banks’ MSME lending businesses.
Addressing MSME Financing Gaps: Explores challenges in traditional credit scoring for Hong Kong’s MSMEs (over 340,000 enterprises, ~45% of private sector employment) and highlights how alternative data can improve access to finance.
Alternative Data & ML Models: Details transactional and non-transactional data sources, machine learning techniques for default prediction, model interpretability, and privacy-enhancing technologies including Federated Learning.
Practical Frameworks: Presents automation of credit underwriting, industry case studies, technical evaluations, and a proposed Retail Alternative Credit Scoring (RACS) demonstration.
Roadmap for Hong Kong: Recommends data infrastructure (e.g., Commercial Data Interchange), government support, and ecosystem collaboration to enable inclusive, responsible lending.
