# Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring

arXiv: https://arxiv.org/abs/2009.13384 · HTML: https://mlsanity.com/paper/2009.13384/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 26 of 26)

- [Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview](https://mlsanity.com/paper/2206.07438/) (2022)
- [Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models](https://mlsanity.com/paper/2310.00566/) (2023)
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- [Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring](https://mlsanity.com/paper/2205.10535/) (2022)
- [SHAP-XRT: The Shapley Value Meets Conditional Independence Testing](https://mlsanity.com/paper/2207.07038/) (2022)
- [Multicriteria interpretability driven Deep Learning](https://mlsanity.com/paper/2111.14088/) (2021)
- [Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots](https://mlsanity.com/paper/2404.18702/) (2024)
- [Glocal Explanations of Expected Goal Models in Soccer](https://mlsanity.com/paper/2308.15559/) (2023)
- [Deep Reputation Scoring in DeFi: zScore-Based Wallet Ranking from Liquidity and Trading Signals](https://mlsanity.com/paper/2507.20494/) (2025)
- [Model Science: getting serious about verification, explanation and control of AI systems](https://mlsanity.com/paper/2508.20040/) (2025)
- [Evaluating the stability of model explanations in instance-dependent cost-sensitive credit scoring](https://mlsanity.com/paper/2509.01409/) (2025)
- [Interpretable Selective Learning in Credit Risk](https://mlsanity.com/paper/2209.10127/) (2022)
- [Explaining and visualizing black-box models through counterfactual paths](https://mlsanity.com/paper/2307.07764/) (2023)
- [Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence](https://mlsanity.com/paper/2312.01555/) (2023)
- [A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting](https://mlsanity.com/paper/2404.13004/) (2024)
- [Best Practices for Responsible Machine Learning in Credit Scoring](https://mlsanity.com/paper/2409.20536/) (2024)
- [Axiomatic Explainer Globalness via Optimal Transport](https://mlsanity.com/paper/2411.01126/) (2024)
- [Privacy Against Agnostic Inference Attacks in Vertical Federated Learning](https://mlsanity.com/paper/2302.05545/) (2023)
- [Pick your Poison: Undetectability versus Robustness in Data Poisoning Attacks](https://mlsanity.com/paper/2305.09671/) (2023)
- [ZAPs: A Reward Attribution Framework for DeFi Ecosystems with Adversarial-Robust Scoring via Parallel Anomaly Ensemble Detection](https://mlsanity.com/paper/2607.27859/) (2026)
- [Shapley value-based approaches to explain the robustness of classifiers in machine learning](https://mlsanity.com/paper/2209.04254/) (2022)
- [Multi-criteria Rank-based Aggregation for Explainable AI](https://mlsanity.com/paper/2505.24612/) (2025)
- [OrdShap: Feature Position Importance for Sequential Black-Box Models](https://mlsanity.com/paper/2507.11855/) (2025)
- [Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings](https://mlsanity.com/paper/2604.22662/) (2026)
- [Communicating Credit Risk with Large Language Models: Evaluation of Explanations from Standard and Alternative Data-Based Models](https://mlsanity.com/paper/2608.17715/) (2026)
- [PaGNet: A Panel-Aware GBDT--Neural Network for Multi-Target Corporate Tax Avoidance Proxy Forecasting](https://mlsanity.com/paper/2609.20177/) (2026)

## Abstract (arXiv)

A major requirement for credit scoring models is to provide a maximally accurate risk prediction. Additionally, regulators demand these models to be transparent and auditable. Thus, in credit scoring, very simple predictive models such as logistic regression or decision trees are still widely used and the superior predictive power of modern machine learning algorithms cannot be fully leveraged. Significant potential is therefore missed, leading to higher reserves or more credit defaults. This paper works out different dimensions that have to be considered for making credit scoring models understandable and presents a framework for making ``black box'' machine learning models transparent, auditable and explainable. Following this framework, we present an overview of techniques, demonstrate how they can be applied in credit scoring and how results compare to the interpretability of score cards. A real world case study shows that a comparable degree of interpretability can be achieved while machine learning techniques keep their ability to improve predictive power.