A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS
This addresses data privacy and compatibility issues in multi-institutional credit scoring, offering a practical solution for companies to collaborate while complying with regulations.
The paper tackles the problem of training accurate credit scoring models across multiple companies without sharing private data, by proposing the MICS framework which enables vertical and horizontal cooperation and shows that companies can jointly train more robust and accurate global models.
As more and more companies store their customers' data; various information of a person is distributed among numerous companies' databases. Different industrial sectors carry distinct features about the same customers. Also, different companies within the same industrial sector carry similar kinds of data about the customers with different data representations. Cooperation between companies from different industrial sectors, called vertical cooperation, and between the companies within the same sector, called horizontal cooperation, can lead to more accurate machine learning models and better estimations in tasks such as credit scoring. However, data privacy regulations and compatibility issues for different data representations are huge obstacles to cooperative model training. By proposing the training framework MICS and experimentation on several numerical data sets, we showed that companies would have an incentive to cooperate with other companies from their sector and with other industrial sectors to jointly train more robust and accurate global models without explicitly sharing their customers' private data.