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ASTRI Leverages “Federated Learning” Tech to Facilitate Credit Scoring

The Hong Kong Applied Science and Technology Research Institute (ASTRI) joins forces with tech-embracing companies to leverage a privacy-preserving technology, called “Federated Learning”, to develop artificial intelligence (AI) models and output in the form of encrypted parameters that serve as a reference for financial institutions to conduct comprehensive credit analyses for micro, small and medium-sized enterprises (MSMEs) to help them get access to financing.

ASTRI’s partners include two major global banks, one of which is the first virtual bank serving MSMEs in Hong Kong; a Hong Kong restaurant guide and review platform; and a logistics and freight pricing platform.

Unlike traditional machine-learning methods, Federated Learning does not require data to be transferred directly to a central database, thus protecting privacy and mitigating the risk of data security breaches. Data partners and financial institutions can establish common credit evaluation models by combining their encrypted parameters.

During the process, the collaborators do not have access to any consumer personal data, nor are the identities of the enterprises identified. Only when an enterprise applies for financing and is undergoing authorisation can the designated financial institution obtain the relevant parameters and conduct a credit evaluation.

The Chief Executive Officer of ASTRI noted that the agency leverages Federated Learning technology to provide alternative data for credit assessment while protecting privacy and data security to help financial institutions reduce the cost of vetting and approving loans for MSMEs and help enterprises get financing.

It is expected that ASTRI will collaborate with more organisations to promote the implementation of open data. The Federated Learning technology will also effectively promote the development of other Fintech applications and support the government’s efforts to drive smart city transformation.

As data partners, the restaurant guide and review platform and the logistics and freight pricing platform will leverage big data, including various restaurant popularity metrics, the transaction status of consignment merchants, and business operation status, to identify the elements affecting the credit risks of enterprises from alternative data by using AI and Federated Learning.

Through this, a model will be trained to derive parameters to assist credit scoring. No information about the enterprises will be transferred from the data partners to other institutions.

With the authorisation of the enterprises applying for loans, the two banks can refer to data providers’ assessments of an enterprise’s competitiveness in its industry and its credits status, which is determined using the enterprise’s operation parameters through the model developed to process an MSME loan application.

During the loan-approval process, financial institutions will be able to make more reliable credit assessments based on the projections of their own credit evaluation models and the assessments of their data partners. The first phase of the models developed using Federated Learning is expected to be in use within 12 months.

The Deputy Chief Executive of the first virtual bank serving MSMEs in Hong Kong noted that with the AI models, the firm can access customers’ comprehensive operational data in real-time to help realise financial inclusion by expediting loan approvals and meeting the financing needs of SMEs.

The CEO and Acting CTO of the Hong Kong restaurant guide and review platform stated that they expect to use Big Data and Federated Learning to train AI models and develop professional industry parameters to make credit applications from small and medium-sized restaurant partners faster and easier to quickly meet their operational needs.

The Co-founder and Director of the logistics and freight pricing platform noted that they are collaborating with ASTRI, using the Federated Learning technology to strictly protect privacy.

The massive data on the platform is used to train AI models to help to build industry parameters, which helps SMEs apply for trade financing and credit with ease, and effectively addresses their financing needs.

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