# Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN

arXiv: https://arxiv.org/abs/2408.03497 · HTML: https://mlsanity.com/paper/2408.03497/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 53 references that are in the map.
- First posted on arXiv 7 Aug 2024.

## Builds on (most cited first, 21 of 21)

- [Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation](https://mlsanity.com/paper/2010.16061/) (2020)
- [PLGSLAM: Progressive Neural Scene Represenation with Local to Global Bundle Adjustment](https://mlsanity.com/paper/2312.09866/) (2023)
- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Pareto Self-Supervised Training for Few-Shot Learning](https://mlsanity.com/paper/2104.07841/) (2021)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [Attention-Enhancing Backdoor Attacks Against BERT-based Models](https://mlsanity.com/paper/2310.14480/) (2023)
- [Regional Style and Color Transfer](https://mlsanity.com/paper/2404.13880/) (2024)
- [CPSeg: Finer-grained Image Semantic Segmentation via Chain-of-Thought Language Prompting](https://mlsanity.com/paper/2310.16069/) (2023)
- [APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking](https://mlsanity.com/paper/2406.14449/) (2024)
- [Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective](https://mlsanity.com/paper/2312.01397/) (2023)
- [Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems](https://mlsanity.com/paper/1902.00615/) (2019)
- [TaskCLIP: Extend Large Vision-Language Model for Task Oriented Object Detection](https://mlsanity.com/paper/2403.08108/) (2024)
- [A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation](https://mlsanity.com/paper/2404.13812/) (2024)
- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2404.17379/) (2024)
- [AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning](https://mlsanity.com/paper/2405.09582/) (2024)
- [Predicting NVIDIA's Next-Day Stock Price: A Comparative Analysis of LSTM, MLP, ARIMA, and ARIMA-GARCH Models](https://mlsanity.com/paper/2405.08284/) (2024)
- [A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission](https://mlsanity.com/paper/2402.02043/) (2024)
- [Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation](https://mlsanity.com/paper/2406.06374/) (2024)
- [An ExplainableFair Framework for Prediction of Substance Use Disorder Treatment Completion](https://mlsanity.com/paper/2404.03833/) (2024)

## Built on by (most cited first, 10 of 10)

- [Identification of Prognostic Biomarkers for Stage III Non-Small Cell Lung Carcinoma in Female Nonsmokers Using Machine Learning](https://mlsanity.com/paper/2408.16068/) (2024)
- [NEVLP: Noise-Robust Framework for Efficient Vision-Language Pre-training](https://mlsanity.com/paper/2409.09582/) (2024)
- [Style Transfer: From Stitching to Neural Networks](https://mlsanity.com/paper/2409.00606/) (2024)
- [Enhancing Skin Lesion Diagnosis with Ensemble Learning](https://mlsanity.com/paper/2409.04381/) (2024)
- [Recoverable Anonymization for Pose Estimation: A Privacy-Enhancing Approach](https://mlsanity.com/paper/2409.02715/) (2024)
- [Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models](https://mlsanity.com/paper/2410.19241/) (2024)
- [Calibrated Credit Intelligence: Shift-Robust and Fair Risk Scoring with Bayesian Uncertainty and Gradient Boosting](https://mlsanity.com/paper/2603.06733/) (2026)
- [AdaMixup: A Dynamic Defense Framework for Membership Inference Attack Mitigation](https://mlsanity.com/paper/2501.02182/) (2025)
- [Redefining Data-Centric Design: A New Approach with a Domain Model and Core Data Ontology for Computational Systems](https://mlsanity.com/paper/2409.09058/) (2024)
- [Innovative Deep Learning Techniques for Obstacle Recognition: A Comparative Study of Modern Detection Algorithms](https://mlsanity.com/paper/2410.10096/) (2024)

## Abstract (arXiv)

Bank credit risk is a significant challenge in modern financial transactions, and the ability to identify qualified credit card holders among a large number of applicants is crucial for the profitability of a bank'sbank's credit card business. In the past, screening applicants'applicants' conditions often required a significant amount of manual labor, which was time-consuming and labor-intensive. Although the accuracy and reliability of previously used ML models have been continuously improving, the pursuit of more reliable and powerful AI intelligent models is undoubtedly the unremitting pursuit by major banks in the financial industry. In this study, we used a dataset of over 40,000 records provided by a commercial bank as the research object. We compared various dimensionality reduction techniques such as PCA and T-SNE for preprocessing high-dimensional datasets and performed in-depth adaptation and tuning of distributed models such as LightGBM and XGBoost, as well as deep models like Tabnet. After a series of research and processing, we obtained excellent research results by combining SMOTEENN with these techniques. The experiments demonstrated that LightGBM combined with PCA and SMOTEENN techniques can assist banks in accurately predicting potential high-quality customers, showing relatively outstanding performance compared to other models.