# Federated Artificial Intelligence for Unified Credit Assessment

arXiv: https://arxiv.org/abs/2105.09484 · HTML: https://mlsanity.com/paper/2105.09484/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 20 May 2021.

## Builds on (most cited first, 4 of 4)

- [An Interpretable Model with Globally Consistent Explanations for Credit Risk](https://mlsanity.com/paper/1811.12615/) (2018)
- [The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics](https://mlsanity.com/paper/2002.09931/) (2020)
- [dpUGC: Learn Differentially Private Representation for User Generated Contents](https://mlsanity.com/paper/1903.10453/) (2019)
- [Generic Multilayer Network Data Analysis with the Fusion of Content and Structure](https://mlsanity.com/paper/1905.08635/) (2019)

## Abstract (arXiv)

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.