# Explainable AI in Credit Risk Management

arXiv: https://arxiv.org/abs/2103.00949 · HTML: https://mlsanity.com/paper/2103.00949/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 1 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Explainable Machine Learning in Deployment](https://mlsanity.com/paper/1909.06342/) (2019)
- [One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques](https://mlsanity.com/paper/1909.03012/) (2019)

## Built on by (most cited first, 19 of 19)

- [Explainability in Practice: A Survey of Explainable NLP Across Various Domains](https://mlsanity.com/paper/2502.00837/) (2025)
- [A Comprehensive Review on Financial Explainable AI](https://mlsanity.com/paper/2309.11960/) (2023)
- [TABCF: Counterfactual Explanations for Tabular Data Using a Transformer-Based VAE](https://mlsanity.com/paper/2410.10463/) (2024)
- [Non-Uniform Interpolation in Integrated Gradients for Low-Latency Explainable-AI](https://mlsanity.com/paper/2302.11107/) (2023)
- [LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information](https://mlsanity.com/paper/2104.06057/) (2021)
- [Interpreting LLMs as Credit Risk Classifiers: Do Their Feature Explanations Align with Classical ML?](https://mlsanity.com/paper/2510.25701/) (2025)
- [TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start](https://mlsanity.com/paper/2311.18749/) (2023)
- [Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach](https://mlsanity.com/paper/2412.04183/) (2024)
- [PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and Models](https://mlsanity.com/paper/2505.10515/) (2025)
- [Building crypto portfolios with agentic AI](https://mlsanity.com/paper/2507.20468/) (2025)
- [Could Large Language Models work as Post-hoc Explainability Tools in Credit Risk Models?](https://mlsanity.com/paper/2602.18895/) (2026)
- [A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection](https://mlsanity.com/paper/2212.02906/) (2022)
- [SpecXAI -- Spectral interpretability of Deep Learning Models](https://mlsanity.com/paper/2302.09949/) (2023)
- [American Option Pricing using Self-Attention GRU and Shapley Value Interpretation](https://mlsanity.com/paper/2310.12500/) (2023)
- [Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System](https://mlsanity.com/paper/2311.11655/) (2023)
- [Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions](https://mlsanity.com/paper/2312.16223/) (2023)
- [A Multi-Layered Research Framework for Human-Centered AI: Defining the Path to Explainability and Trust](https://mlsanity.com/paper/2504.13926/) (2025)
- [Unlocking the Black Box: A Five-Dimensional Framework for Evaluating Explainable AI in Credit Risk](https://mlsanity.com/paper/2511.04980/) (2025)
- [Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models](https://mlsanity.com/paper/2608.17715/) (2026)

## Abstract (arXiv)

Artificial Intelligence (AI) has created the single biggest technology revolution the world has ever seen. For the finance sector, it provides great opportunities to enhance customer experience, democratize financial services, ensure consumer protection and significantly improve risk management. While it is easier than ever to run state-of-the-art machine learning models, designing and implementing systems that support real-world finance applications have been challenging. In large part because they lack transparency and explainability which are important factors in establishing reliable technology and the research on this topic with a specific focus on applications in credit risk management. In this paper, we implement two advanced post-hoc model agnostic explainability techniques called Local Interpretable Model Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to machine learning (ML)-based credit scoring models applied to the open-access data set offered by the US-based P2P Lending Platform, Lending Club. Specifically, we use LIME to explain instances locally and SHAP to get both local and global explanations. We discuss the results in detail and present multiple comparison scenarios by using various kernels available for explaining graphs generated using SHAP values. We also discuss the practical challenges associated with the implementation of these state-of-art eXplainabale AI (XAI) methods and document them for future reference. We have made an effort to document every technical aspect of this research, while at the same time providing a general summary of the conclusions.