Leveraging Data Alliance for Sustainable Digital Transformation in Banking and Financial Services
Party serviced is a virtual bank with over US$20 billion worth in revenue headquartered in Shenzhen, China. Known for its micro-loan validation approach using facial authentication and big data credit ratings, this establishment is living up to its reinvigorated commitment of leveraging blockchain technology as a trust building platform, to promote sustainable digital transformation under an effective Environmental, Social and Governance (ESG) infrastructure.
Party joined force in the project is a government statutory body, dedicated to building a vibrant innovation and technology ecosystem to connect stakeholders, to nurture technology talents, facilitate collaboration, and catalyse innovations, to deliver social and economic benefits to Hong Kong and the region.
This project makes possible secure data alliance, through adoption of “federated learning models” developed for the statutory platform, while complying with cyber security regulations to safeguard personal data of neobank customers.
Designed to integrate data scattered across departments, institutions, or jurisdictions, model co-builders collaborating, yet not sharing or transferring the encrypted data from the hand of its authorized owner, are greatly facilitated to extract and link information to a user identity from the data universe, when undergoing credit rating, for example.
Model Training Deliverables include:
Structuring training portal and mock-up site
Self-assessment checklist on data readiness
Provision of training materials and sample use cases
FSI executive guidebook (web edition)
Model Application Deliverables include:
Gap/Readiness Analyses in advance of model deployment for both data requestors and providers
Project proposal and implementation plan write-up, detailing user requirements, functioning and technical specifications
Global best practice and use cases guidebook (web edition)
Bootcamp to invite respective professionals for project enablement
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