# An Interpretable Model with Globally Consistent Explanations for Credit Risk

arXiv: https://arxiv.org/abs/1811.12615 · HTML: https://mlsanity.com/paper/1811.12615/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 30 of 33)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results](https://mlsanity.com/paper/2104.08015/) (2021)
- [GLOBE-CE: A Translation-Based Approach for Global Counterfactual Explanations](https://mlsanity.com/paper/2305.17021/) (2023)
- [Elephants Never Forget: Testing Language Models for Memorization of Tabular Data](https://mlsanity.com/paper/2403.06644/) (2024)
- [A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations](https://mlsanity.com/paper/2106.02605/) (2021)
- [Recourse under Model Multiplicity via Argumentative Ensembling (Technical Report)](https://mlsanity.com/paper/2312.15097/) (2023)
- [A Review of the Role of Causality in Developing Trustworthy AI Systems](https://mlsanity.com/paper/2302.06975/) (2023)
- [Explainability Is in the Mind of the Beholder: Establishing the Foundations of Explainable Artificial Intelligence](https://mlsanity.com/paper/2112.14466/) (2021)
- [Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring](https://mlsanity.com/paper/2209.10070/) (2022)
- [Trade-off Between Efficiency and Consistency for Removal-based Explanations](https://mlsanity.com/paper/2210.17426/) (2022)
- [Reconciling Model Multiplicity for Downstream Decision Making](https://mlsanity.com/paper/2405.19667/) (2024)
- [Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learning](https://mlsanity.com/paper/2411.04324/) (2024)
- [Extrapolation Frameworks in Cognitive Psychology Suitable for Study of Image Classification Models](https://mlsanity.com/paper/2112.03411/) (2021)
- [Computing Rule-Based Explanations by Leveraging Counterfactuals](https://mlsanity.com/paper/2210.17071/) (2022)
- [Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance](https://mlsanity.com/paper/2301.07060/) (2023)
- [Unpacking the Black Box: Regulating Algorithmic Decisions](https://mlsanity.com/paper/2110.03443/) (2021)
- [Score-Based Explanations in Data Management and Machine Learning: An Answer-Set Programming Approach to Counterfactual Analysis](https://mlsanity.com/paper/2106.10562/) (2021)
- [Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance](https://mlsanity.com/paper/2209.10082/) (2022)
- [Interpretable Selective Learning in Credit Risk](https://mlsanity.com/paper/2209.10127/) (2022)
- [RbX: Region-based explanations of prediction models](https://mlsanity.com/paper/2210.08721/) (2022)
- [Attribution-Scores and Causal Counterfactuals as Explanations in Artificial Intelligence](https://mlsanity.com/paper/2303.02829/) (2023)
- [Attribution-Scores in Data Management and Explainable Machine Learning](https://mlsanity.com/paper/2308.00184/) (2023)
- [LEx: A Framework for Operationalising Layers of Machine Learning Explanations](https://mlsanity.com/paper/2104.09612/) (2021)
- [To what extent should we trust AI models when they extrapolate?](https://mlsanity.com/paper/2201.11260/) (2022)
- [Argumentative Ensembling for Robust Recourse under Model Multiplicity](https://mlsanity.com/paper/2506.20260/) (2025)
- [On Arbitrary Predictions from Equally Valid Models](https://mlsanity.com/paper/2507.19408/) (2025)
- [Distilling Interpretable Models into Human-Readable Code](https://mlsanity.com/paper/2101.08393/) (2021)
- [Federated Artificial Intelligence for Unified Credit Assessment](https://mlsanity.com/paper/2105.09484/) (2021)
- [Constructive Interpretability with CoLabel: Corroborative Integration, Complementary Features, and Collaborative Learning](https://mlsanity.com/paper/2205.10011/) (2022)
- [A Concept and Argumentation based Interpretable Model in High Risk Domains](https://mlsanity.com/paper/2208.08149/) (2022)

## Abstract (arXiv)

We propose a possible solution to a public challenge posed by the Fair Isaac Corporation (FICO), which is to provide an explainable model for credit risk assessment. Rather than present a black box model and explain it afterwards, we provide a globally interpretable model that is as accurate as other neural networks. Our "two-layer additive risk model" is decomposable into subscales, where each node in the second layer represents a meaningful subscale, and all of the nonlinearities are transparent. We provide three types of explanations that are simpler than, but consistent with, the global model. One of these explanation methods involves solving a minimum set cover problem to find high-support globally-consistent explanations. We present a new online visualization tool to allow users to explore the global model and its explanations.