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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | Credit Risk Forecasting in FinTech Using Hybrid Ensemble Models: A Comparative Performance Study |
| Authors Name: | Sohan manmeet sethi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_210342
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| Published Paper Id: | IJRTI2604007 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i4.210342 |
| Abstract: | The credit risk forecasting is one of the pillars of financial stability and especially in the fast-growing FinTech industry where the data diversity and magnitude have made old models less and less sufficient. The performance of hybrid ensemble models, i.e., systems that combine machine learning and deep learning algorithms, is reviewed and compared to predict credit risk in FinTech environments in this paper. Conventional statistical tools like logistic regression are highly interpretable but unable to address multiple high-dimensional and unstructured data. Random Forest and Gradient Boosting are machine learning models that optimize predictive accuracy but do not provide transparency. Hybrid ensemble models, in contrast, combine the best abilities of different models to enhance their accuracy and strength. The review indicates that a hybrid ensemble performs better than an individual learner in the characteristics like AUC, precision, and F1-score at the same time of being flexible to the large scale and real time FinTech applications. Interpretability, computational scalability and ethical fairness are among the major challenges. The paper presents a conclusion that the future needs of research focusing explainable and privacy preserving and fair hybrid ensemble systems should be considered so that transparent and responsible credit risk management in digital finance can be established. |
| Keywords: | Credit Risk Forecasting; FinTech; Hybrid Ensemble Models; Explainable AI; Machine Learning |
| Cite Article: | "Credit Risk Forecasting in FinTech Using Hybrid Ensemble Models: A Comparative Performance Study", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a47-a58, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604007.pdf |
| Downloads: | 000147 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2604007
Registration ID:210342
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i4.210342
Page No: a47-a58 Country: -, -, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604007 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604007 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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