# E.T.-RNN: Applying Deep Learning to Credit Loan Applications

arXiv: https://arxiv.org/abs/1911.02496 · HTML: https://mlsanity.com/paper/1911.02496/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

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

- [DANets: Deep Abstract Networks for Tabular Data Classification and Regression](https://mlsanity.com/paper/2112.02962/) (2021)
- [Consistent Counterfactuals for Deep Models](https://mlsanity.com/paper/2110.03109/) (2021)
- [Leave-one-out Unfairness](https://mlsanity.com/paper/2107.10171/) (2021)
- [TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoning](https://mlsanity.com/paper/2312.09039/) (2023)
- [Adversarial Attacks on Deep Models for Financial Transaction Records](https://mlsanity.com/paper/2106.08361/) (2021)
- [Multimodal Banking Dataset: Understanding Client Needs through Event Sequences](https://mlsanity.com/paper/2409.17587/) (2024)
- [FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approaches](https://mlsanity.com/paper/2103.14539/) (2021)
- [Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge](https://mlsanity.com/paper/2404.02047/) (2024)
- [RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoring](https://mlsanity.com/paper/2206.00568/) (2022)
- [Bank transactions embeddings help to uncover current macroeconomics](https://mlsanity.com/paper/2110.12000/) (2021)
- [Taming Normalizing Flows](https://mlsanity.com/paper/2211.16488/) (2022)
- [Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Users](https://mlsanity.com/paper/2204.11849/) (2022)
- [Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions](https://mlsanity.com/paper/2209.09362/) (2022)
- [Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service](https://mlsanity.com/paper/2312.14941/) (2023)
- [Uniting contrastive and generative learning for event sequences models](https://mlsanity.com/paper/2408.09995/) (2024)
- [LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clients](https://mlsanity.com/paper/2508.10021/) (2025)
- [Inclusive FinTech Lending via Contrastive Learning and Domain Adaptation](https://mlsanity.com/paper/2305.05827/) (2023)
- [DNN2LR: Automatic Feature Crossing for Credit Scoring](https://mlsanity.com/paper/2102.12036/) (2021)
- [Continuous-time convolutions model of event sequences](https://mlsanity.com/paper/2302.06247/) (2023)
- [Vortex Feature Positioning: Bridging Tabular IIoT Data and Image-Based Deep Learning](https://mlsanity.com/paper/2303.09068/) (2023)
- [Designing an attack-defense game: how to increase robustness of financial transaction models via a competition](https://mlsanity.com/paper/2308.11406/) (2023)
- [Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteria](https://mlsanity.com/paper/2309.12620/) (2023)
- [Self-Attention Mechanism in Multimodal Context for Banking Transaction Flow](https://mlsanity.com/paper/2410.08243/) (2024)
- [Looking around you: external information enhances representations for event sequences](https://mlsanity.com/paper/2502.10205/) (2025)
- [TransactionGPT](https://mlsanity.com/paper/2511.08939/) (2025)
- [Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Models](https://mlsanity.com/paper/2604.09085/) (2026)

## Abstract (arXiv)

In this paper we present a novel approach to credit scoring of retail customers in the banking industry based on deep learning methods. We used RNNs on fine grained transnational data to compute credit scores for the loan applicants. We demonstrate that our approach significantly outperforms the baselines based on the customer data of a large European bank. We also conducted a pilot study on loan applicants of the bank, and the study produced significant financial gains for the organization. In addition, our method has several other advantages described in the paper that are very significant for the bank.