A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation

Kenneth Kiprotich Langat; Anthony Gichuhi Waititu; Philip Odhiambo Ngare

doi:doi:10.11648/j.ijdsa.20241003.11

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A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation

Kenneth Kiprotich Langat^*, Anthony Gichuhi Waititu, Philip Odhiambo Ngare

Published in International Journal of Data Science and Analysis (Volume 10, Issue 3)

Received: 12 July 2024 Accepted: 31 July 2024 Published: 22 August 2024

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Abstract

The recent developments in the credit and banking industry brought by technology has led to increased competition and the rise of risks and challenges. Credit scoring is one of the core items that keeps this industry competitive and profitable. The creation of credit score models to assess the ability of the loan applicant to repay his or her loan remains an active field of research. Practically, the existing models ignore the factor of inflation in determining the credit score of a loan applicant. Inflation affect the performance of the financing institution negatively because it makes some of the borrowers struggle to repay the loan and so leading to some bad debts that might end up being written off. By integrating the inflation factor to the Extreme gradient boosting algorithm led to improved accuracy of the model. In this paper, a new model that uses the inflation rate of a specific region or country in the regularization term of the extreme gradient boosting model has been developed. The evaluation of the model is by comparison with the other common models using ROC, Accuracy, precision and recall. The developed model emerge the second best in terms of performance but better than the standard extreme gradient boosting model.

Published in	International Journal of Data Science and Analysis (Volume 10, Issue 3)
DOI	10.11648/j.ijdsa.20241003.11
Page(s)	41-48
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

XGBoost, Inflation, Decision Tree, Credit Analysis

References

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[4]	F. S. D. Kenya, 2016 finaccess household survey,Financial Sector Deepening and Central Bank of Kenya. http://fsdkenya. org/publication/finaccess2016/Accessed Leveraged losses: lessons from the mortgage market meltdown. 2016; pp 2019.
[5]	Izaguirre, J. C., Mazer, C. R., Graham, C. L., & Center, Digital credit market monitoring in Tanzania Slide Deck. 2018.
[6]	Dushimimana, B., Wambui, Y., Lubega, T., & McSharry, Use of machine learning techniques to create a credit score model for airtime loans Journal of Risk and Financial Management. 2020, 13(8), 180. https://doi.org/10.3390/jrfm13080180.
[7]	M. Whitney and H. Richter Lipford, Participatory sensing for community building, in CHI11 Extended Abstracts on Human Factors in Computing Systems. 2011, 1321-1326.
[8]	Shema Effective credit scoring using limited mobile phone data. In Proceedings of of the Tenth International Conference on Information and Communication Technologies and Development, 2019; pp. 1-11.
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[10]	Friedman, J. H. Greedy function approximation: a gradient boosting machine. Annals of statistics. 2001, 1189-1232.
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[15]	K.Wang, M. Li, J. Cheng, X. Zhou, and G. Li, Suk, Research on personal credit risk evaluation based on xgboost, Procedia computer science. 2022, 119, 1128-1135. https://doi.org/10.1016/j.procs.2022.01.143

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APA Style

Langat, K. K., Waititu, A. G., Ngare, P. O. (2024). A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation. International Journal of Data Science and Analysis, 10(3), 41-48. https://doi.org/10.11648/j.ijdsa.20241003.11

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ACS Style

Langat, K. K.; Waititu, A. G.; Ngare, P. O. A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation. Int. J. Data Sci. Anal. 2024, 10(3), 41-48. doi: 10.11648/j.ijdsa.20241003.11

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AMA Style

Langat KK, Waititu AG, Ngare PO. A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation. Int J Data Sci Anal. 2024;10(3):41-48. doi: 10.11648/j.ijdsa.20241003.11

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@article{10.11648/j.ijdsa.20241003.11,
  author = {Kenneth Kiprotich Langat and Anthony Gichuhi Waititu and Philip Odhiambo Ngare},
  title = {A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {3},
  pages = {41-48},
  doi = {10.11648/j.ijdsa.20241003.11},
  url = {https://doi.org/10.11648/j.ijdsa.20241003.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241003.11},
  abstract = {The recent developments in the credit and banking industry brought by technology has led to increased competition and the rise of risks and challenges. Credit scoring is one of the core items that keeps this industry competitive and profitable. The creation of credit score models to assess the ability of the loan applicant to repay his or her loan remains an active field of research. Practically, the existing models ignore the factor of inflation in determining the credit score of a loan applicant. Inflation affect the performance of the financing institution negatively because it makes some of the borrowers struggle to repay the loan and so leading to some bad debts that might end up being written off. By integrating the inflation factor to the Extreme gradient boosting algorithm led to improved accuracy of the model. In this paper, a new model that uses the inflation rate of a specific region or country in the regularization term of the extreme gradient boosting model has been developed. The evaluation of the model is by comparison with the other common models using ROC, Accuracy, precision and recall. The developed model emerge the second best in terms of performance but better than the standard extreme gradient boosting model.},
 year = {2024}
}

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TY  - JOUR
T1  - A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation
AU  - Kenneth Kiprotich Langat
AU  - Anthony Gichuhi Waititu
AU  - Philip Odhiambo Ngare
Y1  - 2024/08/22
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241003.11
DO  - 10.11648/j.ijdsa.20241003.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 41
EP  - 48
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241003.11
AB  - The recent developments in the credit and banking industry brought by technology has led to increased competition and the rise of risks and challenges. Credit scoring is one of the core items that keeps this industry competitive and profitable. The creation of credit score models to assess the ability of the loan applicant to repay his or her loan remains an active field of research. Practically, the existing models ignore the factor of inflation in determining the credit score of a loan applicant. Inflation affect the performance of the financing institution negatively because it makes some of the borrowers struggle to repay the loan and so leading to some bad debts that might end up being written off. By integrating the inflation factor to the Extreme gradient boosting algorithm led to improved accuracy of the model. In this paper, a new model that uses the inflation rate of a specific region or country in the regularization term of the extreme gradient boosting model has been developed. The evaluation of the model is by comparison with the other common models using ROC, Accuracy, precision and recall. The developed model emerge the second best in terms of performance but better than the standard extreme gradient boosting model.
VL  - 10
IS  - 3
ER  -

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Author Information

Kenneth Kiprotich Langat

Department of Mathematics, Pan African Institute of Basic Science Technology and Innovation, Nairobi, Kenya; Department of Mathematics, Egerton University, Nakuru, Kenya

Contact Email
Anthony Gichuhi Waititu

Department of Statistics and Actuarial Sciences, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Philip Odhiambo Ngare

Department of Mathematics, University of Nairobi, Nairobi, Kenya

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Plain Text BibTeX RIS

APA Style

Langat, K. K., Waititu, A. G., Ngare, P. O. (2024). A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation. International Journal of Data Science and Analysis, 10(3), 41-48. https://doi.org/10.11648/j.ijdsa.20241003.11

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ACS Style

Langat, K. K.; Waititu, A. G.; Ngare, P. O. A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation. Int. J. Data Sci. Anal. 2024, 10(3), 41-48. doi: 10.11648/j.ijdsa.20241003.11

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AMA Style

Langat KK, Waititu AG, Ngare PO. A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation. Int J Data Sci Anal. 2024;10(3):41-48. doi: 10.11648/j.ijdsa.20241003.11

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@article{10.11648/j.ijdsa.20241003.11,
  author = {Kenneth Kiprotich Langat and Anthony Gichuhi Waititu and Philip Odhiambo Ngare},
  title = {A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation},
  journal = {International Journal of Data Science and Analysis},
  volume = {10},
  number = {3},
  pages = {41-48},
  doi = {10.11648/j.ijdsa.20241003.11},
  url = {https://doi.org/10.11648/j.ijdsa.20241003.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdsa.20241003.11},
  abstract = {The recent developments in the credit and banking industry brought by technology has led to increased competition and the rise of risks and challenges. Credit scoring is one of the core items that keeps this industry competitive and profitable. The creation of credit score models to assess the ability of the loan applicant to repay his or her loan remains an active field of research. Practically, the existing models ignore the factor of inflation in determining the credit score of a loan applicant. Inflation affect the performance of the financing institution negatively because it makes some of the borrowers struggle to repay the loan and so leading to some bad debts that might end up being written off. By integrating the inflation factor to the Extreme gradient boosting algorithm led to improved accuracy of the model. In this paper, a new model that uses the inflation rate of a specific region or country in the regularization term of the extreme gradient boosting model has been developed. The evaluation of the model is by comparison with the other common models using ROC, Accuracy, precision and recall. The developed model emerge the second best in terms of performance but better than the standard extreme gradient boosting model.},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - A Hybrid Extreme Gradient Boosting Model for Credit Risk Modelling in the Presence of Inflation
AU  - Kenneth Kiprotich Langat
AU  - Anthony Gichuhi Waititu
AU  - Philip Odhiambo Ngare
Y1  - 2024/08/22
PY  - 2024
N1  - https://doi.org/10.11648/j.ijdsa.20241003.11
DO  - 10.11648/j.ijdsa.20241003.11
T2  - International Journal of Data Science and Analysis
JF  - International Journal of Data Science and Analysis
JO  - International Journal of Data Science and Analysis
SP  - 41
EP  - 48
PB  - Science Publishing Group
SN  - 2575-1891
UR  - https://doi.org/10.11648/j.ijdsa.20241003.11
AB  - The recent developments in the credit and banking industry brought by technology has led to increased competition and the rise of risks and challenges. Credit scoring is one of the core items that keeps this industry competitive and profitable. The creation of credit score models to assess the ability of the loan applicant to repay his or her loan remains an active field of research. Practically, the existing models ignore the factor of inflation in determining the credit score of a loan applicant. Inflation affect the performance of the financing institution negatively because it makes some of the borrowers struggle to repay the loan and so leading to some bad debts that might end up being written off. By integrating the inflation factor to the Extreme gradient boosting algorithm led to improved accuracy of the model. In this paper, a new model that uses the inflation rate of a specific region or country in the regularization term of the extreme gradient boosting model has been developed. The evaluation of the model is by comparison with the other common models using ROC, Accuracy, precision and recall. The developed model emerge the second best in terms of performance but better than the standard extreme gradient boosting model.
VL  - 10
IS  - 3
ER  -

Copy | Download