# Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach

arXiv: https://arxiv.org/abs/2412.04183 · HTML: https://mlsanity.com/paper/2412.04183/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 5 Dec 2024.

## Builds on (most cited first, 3 of 3)

- [Explainable AI in Credit Risk Management](https://mlsanity.com/paper/2103.00949/) (2021)
- [Explainable AI for Interpretable Credit Scoring](https://mlsanity.com/paper/2012.03749/) (2020)
- [Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions](https://mlsanity.com/paper/2209.09362/) (2022)

## Built on by (most cited first, 1 of 1)

- [Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis](https://mlsanity.com/paper/2505.00410/) (2025)

## Abstract (arXiv)

The development of computing has made credit scoring approaches possible, with various machine learning (ML) and deep learning (DL) techniques becoming more and more valuable. While complex models yield more accurate predictions, their interpretability is often weakened, which is a concern for credit scoring that places importance on decision fairness. As features of the dataset are a crucial factor for the credit scoring system, we implement Linear Discriminant Analysis (LDA) as a feature reduction technique, which reduces the burden of the models complexity. We compared 6 different machine learning models, 1 deep learning model, and a hybrid model with and without using LDA. From the result, we have found our hybrid model, XG-DNN, outperformed other models with the highest accuracy of 99.45% and a 99% F1 score with LDA. Lastly, to interpret model decisions, we have applied 2 different explainable AI techniques named LIME (local) and Morris Sensitivity Analysis (global). Through this research, we showed how feature reduction techniques can be used without affecting the performance and explainability of the model, which can be very useful in resource-constrained settings to optimize the computational workload.