# Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring

arXiv: https://arxiv.org/abs/2205.10535 · HTML: https://mlsanity.com/paper/2205.10535/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 21 May 2022.

## Builds on (most cited first, 4 of 4)

- [Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring](https://mlsanity.com/paper/2009.13384/) (2020)
- [Deep learning in business analytics and operations research: Models, applications and managerial implications](https://mlsanity.com/paper/1806.10897/) (2018)
- [On Reinforcement Learning for Full-length Game of StarCraft](https://mlsanity.com/paper/1809.09095/) (2018)
- [Time to Die: Death Prediction in Dota 2 using Deep Learning](https://mlsanity.com/paper/1906.03939/) (2019)

## Built on by (most cited first, 6 of 6)

- [Empowering Many, Biasing a Few: Generalist Credit Scoring through Large Language Models](https://mlsanity.com/paper/2310.00566/) (2023)
- [Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge](https://mlsanity.com/paper/2404.02047/) (2024)
- [Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data](https://mlsanity.com/paper/2410.03705/) (2024)
- [LendNova: Towards Automated Credit Risk Assessment with Language Models](https://mlsanity.com/paper/2601.02573/) (2026)
- [AutoMode-ASR: Learning to Select ASR Systems for Better Quality and Cost](https://mlsanity.com/paper/2409.12476/) (2024)
- [A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction](https://mlsanity.com/paper/2609.16102/) (2026)

## Abstract (arXiv)

Artificial intelligence (AI) and machine learning (ML) have become vital to remain competitive for financial services companies around the globe. The two models currently competing for the pole position in credit risk management are deep learning (DL) and gradient boosting machines (GBM). This paper benchmarked those two algorithms in the context of credit scoring using three distinct datasets with different features to account for the reality that model choice/power is often dependent on the underlying characteristics of the dataset. The experiment has shown that GBM tends to be more powerful than DL and has also the advantage of speed due to lower computational requirements. This makes GBM the winner and choice for credit scoring. However, it was also shown that the outperformance of GBM is not always guaranteed and ultimately the concrete problem scenario or dataset will determine the final model choice. Overall, based on this study both algorithms can be considered state-of-the-art for binary classification tasks on structured datasets, while GBM should be the go-to solution for most problem scenarios due to easier use, significantly faster training time, and superior accuracy.