# A Vertical Federated Learning Method for Interpretable Scorecard and Its Application in Credit Scoring

arXiv: https://arxiv.org/abs/2009.06218 · HTML: https://mlsanity.com/paper/2009.06218/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Sep 2020.

## Built on by (most cited first, 16 of 16)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [Applications of Federated Learning in Smart Cities: Recent Advances, Taxonomy, and Open Challenges](https://mlsanity.com/paper/2102.01375/) (2021)
- [Vertical Federated Learning for Effectiveness, Security, Applicability: A Survey](https://mlsanity.com/paper/2405.17495/) (2024)
- [Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond](https://mlsanity.com/paper/2207.09087/) (2022)
- [Eliminating Label Leakage in Tree-Based Vertical Federated Learning](https://mlsanity.com/paper/2307.10318/) (2023)
- [IFedAvg: Interpretable Data-Interoperability for Federated Learning](https://mlsanity.com/paper/2107.06580/) (2021)
- [A Vertical Federated Learning Framework for Horizontally Partitioned Labels](https://mlsanity.com/paper/2106.10056/) (2021)
- [Unlearning Clients, Features and Samples in Vertical Federated Learning](https://mlsanity.com/paper/2501.13683/) (2025)
- [A Vertical Federated Learning Method For Multi-Institutional Credit Scoring: MICS](https://mlsanity.com/paper/2111.09038/) (2021)
- [The Effects of Data Imbalance Under a Federated Learning Approach for Credit Risk Forecasting](https://mlsanity.com/paper/2401.07234/) (2024)
- [Vertical Federated Learning Hybrid Local Pre-training](https://mlsanity.com/paper/2405.11884/) (2024)
- [An Information-Theoretic Analysis of The Cost of Decentralization for Learning and Inference Under Privacy Constraints](https://mlsanity.com/paper/2110.05014/) (2021)
- [The Landscape of Modern Machine Learning: A Review of Machine, Distributed and Federated Learning](https://mlsanity.com/paper/2312.03120/) (2023)
- [On the Inference (In-)Security of Vertical Federated Learning: Efficient Auditing against Inference Tampering Attack](https://mlsanity.com/paper/2507.02376/) (2025)
- [Trans-XFed: An Explainable Federated Learning for Supply Chain Credit Assessment](https://mlsanity.com/paper/2508.13715/) (2025)
- [Understanding Backdoor Vulnerabilities in Vertical Federated Learning: The Gap Between Research and Practice](https://mlsanity.com/paper/2608.12962/) (2026)

## Abstract (arXiv)

With the success of big data and artificial intelligence in many fields, the applications of big data driven models are expected in financial risk management especially credit scoring and rating. Under the premise of data privacy protection, we propose a projected gradient-based method in the vertical federated learning framework for the traditional scorecard, which is based on logistic regression with bounded constraints, namely FL-LRBC. The latter enables multiple agencies to jointly train an optimized scorecard model in a single training session. It leads to the formation of the model with positive coefficients, while the time-consuming parameter-tuning process can be avoided. Moreover, the performance in terms of both AUC and the Kolmogorov-Smirnov (KS) statistics is significantly improved due to data enrichment using FL-LRBC. At present, FL-LRBC has already been applied to credit business in a China nation-wide financial holdings group.