# Machine Learning approach for Credit Scoring

arXiv: https://arxiv.org/abs/2008.01687 · HTML: https://mlsanity.com/paper/2008.01687/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2020.

## Built on by (most cited first, 5 of 5)

- [A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms](https://mlsanity.com/paper/2208.13251/) (2022)
- [Top K Ranking for Multi-Armed Bandit with Noisy Evaluations](https://mlsanity.com/paper/2112.06517/) (2021)
- [Deep Reputation Scoring in DeFi: zScore-Based Wallet Ranking from Liquidity and Trading Signals](https://mlsanity.com/paper/2507.20494/) (2025)
- [Explaining Credit Risk Scoring through Feature Contribution Alignment with Expert Risk Analysts](https://mlsanity.com/paper/2103.08359/) (2021)
- [Temporal-Aligned Meta-Learning for Risk Management: A Stacking Approach for Multi-Source Credit Scoring](https://mlsanity.com/paper/2601.07588/) (2026)

## Abstract (arXiv)

In this work we build a stack of machine learning models aimed at composing a state-of-the-art credit rating and default prediction system, obtaining excellent out-of-sample performances. Our approach is an excursion through the most recent ML / AI concepts, starting from natural language processes (NLP) applied to economic sectors' (textual) descriptions using embedding and autoencoders (AE), going through the classification of defaultable firms on the base of a wide range of economic features using gradient boosting machines (GBM) and calibrating their probabilities paying due attention to the treatment of unbalanced samples. Finally we assign credit ratings through genetic algorithms (differential evolution, DE). Model interpretability is achieved by implementing recent techniques such as SHAP and LIME, which explain predictions locally in features' space.