# Explainable AI for Interpretable Credit Scoring

arXiv: https://arxiv.org/abs/2012.03749 · HTML: https://mlsanity.com/paper/2012.03749/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 18 of 18)

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- [Explaining Credit Risk Scoring through Feature Contribution Alignment with Expert Risk Analysts](https://mlsanity.com/paper/2103.08359/) (2021)
- [Linear Discriminant Analysis in Credit Scoring: A Transparent Hybrid Model Approach](https://mlsanity.com/paper/2412.04183/) (2024)
- [Class-Imbalanced-Aware Adaptive Dataset Distillation for Scalable Pretrained Model on Credit Scoring](https://mlsanity.com/paper/2501.10677/) (2025)
- [Holistic Explainable AI (H-XAI): Extending Transparency Beyond Developers in AI-Driven Decision Making](https://mlsanity.com/paper/2508.05792/) (2025)
- [I Know Therefore I Score: Label-Free Crafting of Scoring Functions using Constraints Based on Domain Expertise](https://mlsanity.com/paper/2203.10085/) (2022)
- [Generating detailed saliency maps using model-agnostic methods](https://mlsanity.com/paper/2209.09202/) (2022)
- [Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System](https://mlsanity.com/paper/2311.11655/) (2023)
- [WISCA: A Consensus-Based Approach to Harmonizing Interpretability in Tabular Datasets](https://mlsanity.com/paper/2506.06455/) (2025)
- [Unlocking the Black Box: A Five-Dimensional Framework for Evaluating Explainable AI in Credit Risk](https://mlsanity.com/paper/2511.04980/) (2025)
- [NOMADD: Numerical Optimization of Models Adapting to Data Drift](https://mlsanity.com/paper/2608.02845/) (2026)

## Abstract (arXiv)

With the ever-growing achievements in Artificial Intelligence (AI) and the recent boosted enthusiasm in Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. Credit scoring helps financial experts make better decisions regarding whether or not to accept a loan application, such that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. An interesting concept that has been recently introduced is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance-based) that are required by different people in different situations. Evaluation through the use of functionallygrounded, application-grounded and human-grounded analysis show that the explanations provided are simple, consistent as well as satisfy the six predetermined hypotheses testing for correctness, effectiveness, easy understanding, detail sufficiency and trustworthiness.