About this document
Fuzzy Logic for Explainable AI in Banking by Lingha Dharshan Anparasu is a document available to read on EtoBox.
This document proposes a risk management framework for implementing AI in banking that considers explainability. It evaluates three machine learning approaches (neural networks, logistic regression, and type-2 fuzzy logic) on nine banking use cases. Type-2 fuzzy logic models deliver performance comparable to neural networks but outperform on explainability, making them suitable for automated decision making in financial services. The paper also reviews emerging regulatory guidance on ethical and safe AI ado
- Author
- Lingha Dharshan Anparasu
- Language
- EN