Federated Artificial Intelligence for Unified Credit Assessment
Digital footprints such as social networks or mobile data are a promising data source for evaluating credit inquiries or loan applications. These types of data can cover the deficiency of information, especially in developing countries where transactions are still predominantly facilitated in cash.
A recent study on arXiv.org proposes a federated credit assessment framework. It intends to unify, aggregate, classify and fulfill data in different types and semantics from a variety of sources.
The proposed unified credit score relies on four dimensions: financial, social, contextual, and technological. In order to protect the privacy of customers, the neural network captures the sensitivities of data and decides the degree of representation learning. The suggested framework draws out many implications for academic institutions, businesses, and developers.
With the rapid adoption of Internet technologies, digital footprints have become ubiquitous and versatile to revolutionise the financial industry in digital transformation. This paper takes initiatives to investigate a new paradigm of the unified credit assessment with the use of federated artificial intelligence. We conceptualised digital human representation which consists of social, contextual, financial and technological dimensions to assess the commercial creditworthiness and social reputation of both banked and unbanked individuals. A federated artificial intelligence platform is proposed with a comprehensive set of system design for efficient and effective credit scoring. The study considerably contributes to the cumulative development of financial intelligence and social computing. It also provides a number of implications for academic bodies, practitioners, and developers of financial technologies.
Research paper: Hoang, M.-D., Le, L., Nguyen, A.-T., Le, T., and Nguyen, H. D., “Federated Artificial Intelligence for Unified Credit Assessment”, 2021. Link: https://arxiv.org/abs/2105.09484