mlsanityData to 7 Oct 2026

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

arXiv 1911.02496 ↗First posted 6 Nov 2019cs.LGcited by 26

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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.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 4; 2023: 7; 2024: 4; 2025: 3; 2026: 1.

26 papers.

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  1. 2021DANets: Deep Abstract Networks for Tabular Data Classification and Regressioncited by 50
  2. 2021Consistent Counterfactuals for Deep Modelscited by 37
  3. 2021Leave-one-out Unfairnesscited by 36
  4. 2023TAP4LLM: Table Provider on Sampling, Augmenting, and Packing Semi-structured Data for Large Language Model Reasoningcited by 32
  5. 2021Adversarial Attacks on Deep Models for Financial Transaction Recordscited by 24
  6. 2024Multimodal Banking Dataset: Understanding Client Needs through Event Sequencescited by 9
  7. 2021FeatureEnVi: Visual Analytics for Feature Engineering Using Stepwise Selection and Semi-Automatic Extraction Approachescited by 7
  8. 2022RMT-Net: Reject-aware Multi-Task Network for Modeling Missing-not-at-random Data in Financial Credit Scoringcited by 5
  9. 2024Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledgecited by 5
  10. 2021Bank transactions embeddings help to uncover current macroeconomicscited by 3
  11. 2022Taming Normalizing Flowscited by 3
  12. 2022Heterogeneous Information Network based Default Analysis on Banking Micro and Small Enterprise Userscited by 2
14 more
  1. 2022Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisionscited by 2
  2. 2023Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Servicecited by 2
  3. 2024Uniting contrastive and generative learning for event sequences modelscited by 2
  4. 2025LATTE: Learning Aligned Transactions and Textual Embeddings for Bank Clientscited by 2
  5. 2023Inclusive FinTech Lending via Contrastive Learning and Domain Adaptationcited by 1
  6. 2021DNN2LR: Automatic Feature Crossing for Credit Scoringcited by 0
  7. 2023Continuous-time convolutions model of event sequencescited by 0
  8. 2023Vortex Feature Positioning: Bridging Tabular IIoT Data and Image-Based Deep Learningcited by 0
  9. 2023Designing an attack-defense game: how to increase robustness of financial transaction models via a competitioncited by 0
  10. 2023Data-driven Preference Learning Methods for Sorting Problems with Multiple Temporal Criteriacited by 0
  11. 2024Self-Attention Mechanism in Multimodal Context for Banking Transaction Flowcited by 0
  12. 2025Looking around you: external information enhances representations for event sequencescited by 0
  13. 2025TransactionGPTcited by 0
  14. 2026Beyond Isolated Clients: Integrating Graph-Based Embeddings into Event Sequence Modelscited by 0