A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS

TL;DR

This paper introduces MICS, a vertical federated learning framework enabling multiple companies across sectors to collaboratively train accurate credit scoring models without sharing private data, addressing privacy and compatibility challenges.

Contribution

The paper proposes a novel federated learning method for multi-institutional credit scoring that preserves privacy and handles heterogeneous data representations.

Findings

MICS improves model accuracy in multi-institutional credit scoring.

Companies are incentivized to cooperate across sectors using MICS.

Experimental results validate the effectiveness of the proposed framework.

Abstract

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.