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- Vol. 5 Issue 3
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Predicting Consumer Credit Reporting Dispute Outcomes Using Distilbert and XGboost Ensemble Learning
Authors
1 2 3 Department of Computer Science and IT, School of Computing and Mathematics, The Co-operative University of Kenya, Nairobi, Kenya.
Article Information
- Published In
- Volume 5, Issue 3 (2026)
- Published Online
- September-December 2026
- Pages
- 594-602
- Article Type
- Original Article
References
1. Consumer Financial Protection Bureau. Consumer Complaint Database: Annual Report. Technical report, Consumer Financial Protection Bureau, Washington, D.C., 2024.
2. Richard L Oliver. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4):460–469, 1980.
3. Richard A Spreng, Scott B MacKenzie, and Richard W Olshavsky. A Reexamination of the Determinants of Consumer Satisfaction. Journal of Marketing, 60(3):15–32, 1996.
4. Francisco Villarroel Ordenes, Babis Theodoulidis, Jamie Burton, Thorsten Gruber, and Mo-hamed Zaki. Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach. Journal of Service Research, 17(3):278–295, 2014.
5. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In Advances in Neural Infor-mation Processing Systems, volume 30, pages 5998–6008, 2017.
6. Fabrizio Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002.
7. Thorsten Joachims. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proceedings of the 10th European Conference on Machine Learning, pages 137–142. Springer, 1998.
8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4171–4186, 2019.
9. Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, 2019.
10. Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. How to Fine-Tune BERT for Text Classification? In Chinese Computational Linguistics, pages 194–206. Springer, 2019.
11. Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In Proceed-ings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794, 2016.
12. Candice Bentejac, Anna Csorgo, and Gonzalo Martínez-Munoz. A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3):1937–1967, 2021.
13. Vadim Borisov, Tobias Kudryavtsev, Khaled Balagansky, Andreas Mayr, and Hristijan Gjoreski. Deep Neural Networks and Tabular Data: A Survey. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8345–8361, 2022.
14. A B Haque, A S Rahman, S Jamil, and M Khan. Complaint Classification Using Deep Learn-ing: A Study on CFPB Data. In 2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE), pages 57–62. IEEE, 2021.
15. David Opeoluwa Oyewola, Temidayo Oluwatosin Omotehinwa, and Emmanuel Gbenga Dada. Consumer complaints of consumer financial protection bureau via two-stage residual one-dimensional convolutional neural network (TSR1DCNN). Data and Information Man-agement, 7(3):100046, 2023.
16. Ghadah Alarifi, Mst Farjana Rahman, and Md Shamim Hossain. Prediction and Analysis of Customer Complaints Using Machine Learning Techniques. International Journal of E-Business Research (IJEBR), 19(1):1–25, 2023.
17. Zhuoer Wang, Sizhen Zhu, and Xiongyu Chen. From Complaint Narratives to Monetary Re-lief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints. arXiv preprint arXiv:2606.22664, 2026.
18. Scott M Lundberg and Su-In Lee. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems, volume 30, pages 4765–4774, 2017.
19. Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning: Limi-tations and Opportunities. fairmlbook.org, 2019.
20. Cynthia Rudin. Stop Explaining Black Box Machine Learning Models for High Stakes De-cisions and Use Interpretable Models Instead. Nature Machine Intelligence, 1(5):206–215, 2019.
2. Richard L Oliver. A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4):460–469, 1980.
3. Richard A Spreng, Scott B MacKenzie, and Richard W Olshavsky. A Reexamination of the Determinants of Consumer Satisfaction. Journal of Marketing, 60(3):15–32, 1996.
4. Francisco Villarroel Ordenes, Babis Theodoulidis, Jamie Burton, Thorsten Gruber, and Mo-hamed Zaki. Analyzing Customer Experience Feedback Using Text Mining: A Linguistics-Based Approach. Journal of Service Research, 17(3):278–295, 2014.
5. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. Attention is All you Need. In Advances in Neural Infor-mation Processing Systems, volume 30, pages 5998–6008, 2017.
6. Fabrizio Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1–47, 2002.
7. Thorsten Joachims. Text Categorization with Support Vector Machines: Learning with Many Relevant Features. In Proceedings of the 10th European Conference on Machine Learning, pages 137–142. Springer, 1998.
8. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4171–4186, 2019.
9. Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter, 2019.
10. Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. How to Fine-Tune BERT for Text Classification? In Chinese Computational Linguistics, pages 194–206. Springer, 2019.
11. Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In Proceed-ings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785–794, 2016.
12. Candice Bentejac, Anna Csorgo, and Gonzalo Martínez-Munoz. A comparative analysis of gradient boosting algorithms. Artificial Intelligence Review, 54(3):1937–1967, 2021.
13. Vadim Borisov, Tobias Kudryavtsev, Khaled Balagansky, Andreas Mayr, and Hristijan Gjoreski. Deep Neural Networks and Tabular Data: A Survey. IEEE Transactions on Neural Networks and Learning Systems, 34(11):8345–8361, 2022.
14. A B Haque, A S Rahman, S Jamil, and M Khan. Complaint Classification Using Deep Learn-ing: A Study on CFPB Data. In 2021 International Conference on Computing, Electronics & Communications Engineering (iCCECE), pages 57–62. IEEE, 2021.
15. David Opeoluwa Oyewola, Temidayo Oluwatosin Omotehinwa, and Emmanuel Gbenga Dada. Consumer complaints of consumer financial protection bureau via two-stage residual one-dimensional convolutional neural network (TSR1DCNN). Data and Information Man-agement, 7(3):100046, 2023.
16. Ghadah Alarifi, Mst Farjana Rahman, and Md Shamim Hossain. Prediction and Analysis of Customer Complaints Using Machine Learning Techniques. International Journal of E-Business Research (IJEBR), 19(1):1–25, 2023.
17. Zhuoer Wang, Sizhen Zhu, and Xiongyu Chen. From Complaint Narratives to Monetary Re-lief: A Hybrid Machine Learning Framework for CFPB Consumer Complaints. arXiv preprint arXiv:2606.22664, 2026.
18. Scott M Lundberg and Su-In Lee. A Unified Approach to Interpreting Model Predictions. In Advances in Neural Information Processing Systems, volume 30, pages 4765–4774, 2017.
19. Solon Barocas, Moritz Hardt, and Arvind Narayanan. Fairness and Machine Learning: Limi-tations and Opportunities. fairmlbook.org, 2019.
20. Cynthia Rudin. Stop Explaining Black Box Machine Learning Models for High Stakes De-cisions and Use Interpretable Models Instead. Nature Machine Intelligence, 1(5):206–215, 2019.
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