Finite-Difference Approximation for Explaining Neural Network Predictions in Financial Credit Evaluation

Document Type

Conference Proceeding

Source of Publication

Proceedings of the 2025 International Conference on Advanced Machine Learning and Data Science Amlds 2025

Publication Date

10-6-2025

Abstract

Financial credit evaluation is an essential process that enables lenders to assess the creditworthiness of applicants. While deep learning (DL) models have shown high predictive accuracy in financial risk assessment, their lack of interpretability limits trust and regulatory compliance. Explainable AI (XAI) methods such as SHAP and LIME offer feature attribution, but they suffer from instability and reliance on external data. To address these challenges, this paper introduces the One-sided Linear Gradient Approximation (OLGA), a deterministic and computationally efficient explainability method based on finite-difference approximations and direct perturbation. OLGA provides direct attributions without requiring background data or extensive sampling, making it scalable for high-dimensional financial datasets. We compare OLGA against SHAP and LIME using key explanation metrics, including infidelity, sparsity, and sensitivity, across four deep learning architectures: Multi-Layer Perceptron, Convolutional Neural Network, Transformer, and Autoencoder. Experimental results indicate that OLGA achieves competitive fidelity and provides computational efficiency and stability compared to LIME and SHAP.

ISBN

[9798331510992]

Publisher

IEEE

First Page

12

Last Page

18

Disciplines

Computer Sciences

Keywords

Deep Learning, Explainable AI, Financial Credit Evaluation, Interpretability, LIME, OLGA, SHAP

Scopus ID

105019037906

Indexed in Scopus

yes

Open Access

no

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