Systematic Literature Review: Identifying Key Variables and Measuring Maximum Loan Limits
DOI:
https://doi.org/10.31961/eltikom.v8i2.1156Keywords:
Credit loan, Loan Limit, PRISMA, SLRAbstract
This systematic literature review aims to identify key variables and measurement methods for determining maximum credit loan limits, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The complexity of setting an optimal credit limit to manage credit risk effectively presents a significant challenge. Establishing an efficient maximum loan limit is essential to mitigate credit risk, as an overly high limit increases default potential, while an excessively low limit restricts the financial institution's growth. This study identifies key variables and measurement methods, including Machine Learning techniques, Neural Networks, and traditional statistical approaches. Machine Learning models, such as Random Forest and Gradient Boosting, often surpass traditional methods in handling large, unstructured datasets due to their capacity for modeling complex, non-linear relationships. Conversely, traditional methods like logistic regression may be more suitable for smaller datasets, offering better interpretability and ease of use. The results indicate that systematic variable identification and the use of appropriate measurement methods can enable financial institutions to manage credit loan risk more effectively, supporting the development of sound credit policies.
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G. Li, X. Wang, D. Bi, and J. Hou, “Risk Measurement of the Financial Credit Industry Driven by Data: Based on DAE-LSTM Deep Learning Algorithm,” J. Glob. Inf. Manag., vol. 30, no. 11, 2022, doi: 10.4018/JGIM.308806.
P. Sharifi, V. Jain, M. Arab Poshtkohi, E. seyyedi, and V. Aghapour, “Banks Credit Risk Prediction with Optimized ANN Based on Improved Owl Search Algorithm,” Math. Probl. Eng., vol. 2021, 2021, doi: 10.1155/2021/8458501.
E. Duman, M. S. Aktas, and E. Yahsi, “Credit Risk Prediction Based on Psychometric Data,” Computers, vol. 12, no. 12, pp. 1–16, 2023, doi: 10.3390/computers12120248.
S. Haloui and A. E. El Moudden, “An optimal prediction model’s credit risk: The implementation of the backward elimination and forward regression method,” Int. J. Adv. Comput. Sci. Appl., vol. 11, no. 2, pp. 457–467, 2020, doi: 10.14569/ijacsa.2020.0110259.
I. Emmanuel, Y. Sun, and Z. Wang, “A machine learning-based credit risk prediction engine system using a stacked classifier and a filter-based feature selection method,” J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-024-00882-0.
N. V. Gryzunova, V. I. Pyatanova, V. V. Manuylenko, and K. V. Ordov, “Entrepreneurship and Sustainability Issues Models of Credit Limit-Setting for Companies As a Means of Encouraging Competitiveness * Entrepreneurship and Sustainability Issues,” vol. 7, no. 1, pp. 615–625, 2019.
B. Engelmann, “Spurious Default Probability Projections in Credit Risk Stress Testing Models,” Soc. Sci. Res. Netw., 2024, doi: https://www.doi.org/10.2139/ssrn.4697441.
L. Md., Rezaul, Islam., Md., Shariful, Islam., Sharmin, Shama., Amit, Roy, Chowdhury., Md., Masudul, Hasan, “Enhancing Bank Loan Approval Efficiency Using Machine Learning: An Ensemble Model Approach,” Eng. Technol. J., vol. 09, no. 07, 2024, doi: https://www.doi.org/10.47191/etj/v9i07.24.
J. P. Noriega, L. A. Rivera, and J. A. Herrera, “Machine Learning for Credit Risk Prediction: A Systematic Literature Review,” Data, vol. 8, no. 11, pp. 1–17, 2023, doi: 10.3390/data8110169.
Z. Zhao, T. Cui, S. Ding, J. Li, and A. G. Bellotti, “Resampling Techniques Study on Class Imbalance Problem in Credit Risk Predic-tion,” Mathematics, vol. 12, no. 5, pp. 1–27, 2024, doi: 10.3390/math12050701.
Y. Zhang, Z. Huang, L. Shi, and J. Zou, “A Novel DBN-EFA-CFA-Based Dimensional Reduation for Credit Risk Measurement,” Wu-han Univ. J. Nat. Sci., vol. 28, no. 2, pp. 117–128, 2023, doi: 10.1051/wujns/2023282117.
M. Pang and Z. Li, “A novel profit-based validity index approach for feature selection in credit risk prediction,” AIMS Math., vol. 9, no. 1, pp. 974–997, 2024, doi: 10.3934/math.2024049.
N. I. Hasan, H. Elghareeb, F. F. Farahat, and A. AboElfotouh, “A Proposed Fuzzy Model for Reducing the Risk of Insolvent Loans in the Credit Sector as Applied in Egypt,” Int. J. Fuzzy Log. Intell. Syst., vol. 21, no. 1, pp. 66–75, 2021, doi: 10.5391/IJFIS.2021.21.1.66.
S. Sangwan, N. C. Nayak, Harshita, and V. Sangwan, “Borrowers’ credit risk factors, perception towards repayment interventions and moral hazard in loan delinquency: an investigation of Indian microfinance institutions,” Appl. Econ., vol. 53, no. 56, pp. 6554–6569, 2021, doi: 10.1080/00036846.2021.1946478.
L. Yu and X. Zhang, “Can small sample dataset be used for efficient internet loan credit risk assessment? Evidence from online peer to peer lending,” Financ. Res. Lett., vol. 38, p. 101521, 2021, doi: 10.1016/j.frl.2020.101521.
B. S. Renjing Liu, Xuming Yang, Xinyu Dong, “Credit Risk Assessment Of Banks’ Loan Enterprise Customer Based On State-Constraint,” Comput. Informatics, vol. 40, pp. 145–168, 2021, doi: 10.31577/cai 2021 1 145 CREDIT.
S. Kokate and M. S. R. Chetty, “Credit risk assessment of loan defaulters in commercial banks using voting classifier ensemble learner machine learning model,” Int. J. Saf. Secur. Eng., vol. 11, no. 5, pp. 565–572, 2021, doi: 10.18280/IJSSE.110508.
A. Pérez-Martín, A. Pérez-Torregrosa, A. Rabasa, and M. Vaca, “Feature selection to optimize credit banking risk evaluation decisions for the example of home equity loans,” Mathematics, vol. 8, no. 11, pp. 1–16, 2020, doi: 10.3390/math8111971.
A. Martín-Oliver, S. Ruano, and V. Salas-Fumás, “How does bank competition affect credit risk? Evidence from loan-level data,” Econ. Lett., vol. 196, 2020, doi: 10.1016/j.econlet.2020.109524.
L. Jingming, L. Xuhui, D. Daoming, R. Sumei, and Z. Xuhui, “Research on Credit Risk Measurement of Small and Micro Enterprises Based on the Integrated Algorithm of Improved GSO and ELM,” Math. Probl. Eng., vol. 2020, 2020, doi: 10.1155/2020/3490536.
E. Sivasankar, C. Selvi, and S. Mahalakshmi, “Rough set-based feature selection for credit risk prediction using weight-adjusted boost-ing ensemble method,” Soft Comput., vol. 24, no. 6, pp. 3975–3988, 2020, doi: 10.1007/s00500-019-04167-0.
T. Ayhan and T. Uçar, “Determining customer limits by data mining methods in credit allocation process,” Int. J. Electr. Comput. Eng., vol. 12, no. 2, pp. 1910–1915, 2022, doi: 10.11591/ijece.v12i2.pp1910-1915.
A. Shema, “Effects of increasing credit limit in digital microlending: A study of airtime lending in East Africa,” Electron. J. Inf. Syst. Dev. Ctries., vol. 88, no. 3, pp. 1–14, 2022, doi: 10.1002/isd2.12199.
A. K. Sorokin, “Modeling of Optimal Credit Limits in Microfinance Organizations,” High. Sch. Econ. Econ. J., vol. 26, no. 2, pp. 285–306, 2022, doi: https://www.doi.org/10.17323/1813-8691-2022-26-2-285-306.
W. Gatt, “Loan-to-value limits as a macroprudential policy tool: Developments in theory and practice,” J. Econ. Surv., vol. 38, no. 1, pp. 232–267, 2024, doi: 10.1111/joes.12548.
D. Gilchrist, J. Yu, and R. Zhong, “The limits of green finance: A survey of literature in the context of green bonds and green loans,” Sustain., vol. 13, no. 2, pp. 1–12, 2021, doi: 10.3390/su13020478.
V. Pontines, “The real effects of loan-to-value limits: empirical evidence from Korea,” Empir. Econ., vol. 61, no. 3, pp. 1311–1350, 2021, doi: 10.1007/s00181-020-01908-1.
J. K. Brar, A. Kornprobst, W. J. Braun, M. Davison, and W. Hare, “A case study of the impact of climate change on agricultural loan credit risk,” Mathematics, vol. 9, no. 23, pp. 1–23, 2021, doi: 10.3390/math9233058.
B. Kiviat, “The Moral Limits of Predictive Practices: The Case of Credit-Based Insurance Scores,” Am. Sociol. Rev., vol. 84, no. 6, pp. 1134–1158, 2019, doi: 10.1177/0003122419884917.
J. Gomez-Biscarri, G. López-Espinosa, and A. Mesa-Toro, “The risk implications of the business loan activity in credit unions,” J. Fi-nanc. Stab., vol. 56, no. August, 2021, doi: 10.1016/j.jfs.2021.100932.
N. S. Pham and H. Pham, “Effects of credit limit on efficiency and welfare in a simple general equilibrium model,” Int. J. Econ. Theory, vol. 17, no. 4, pp. 446–470, 2021, doi: 10.1111/ijet.12245.
S. Bagui and J. Walker, “Synthetic feature generation to improve accuracy in prediction of credit limits,” BOHR Int. J. Comput. Sci., vol. 2, no. 1, pp. 44–58, 2023, doi: 10.54646/bijcs.2023.20.
A. Ganga, S. Vijayalakshmi, and R. A. Kalpana, “Deciding bank client’s credit limit and home loan using machine learning,” Adv. Parallel Comput., vol. 38, pp. 288–293, 2021, doi: 10.3233/APC210053.
T. U. Tuğçe Ayhan, “Determining customer limits by data mining methods in credit allocation process,” IJECE, vol. 12, no. 2, pp. 1910–1910, 2022, doi: https://www.doi.org/10.11591/ijece.v12i2.pp1910-1915.
T. Ting, Liang., Guanxiong, Zeng., Qiwei, Zhong., Jianfeng, Chi., Jinghua, Feng., Xiang, Ao., Jiayu, “Credit Risk and Limits Forecasting in E-Commerce Consumer Lending Service via Multi-view-aware Mixture-of-experts Nets,” pp. 229–237, 2021, doi: https://www.doi.org/10.1145/3437963.3441743.
X. Xiao, Yi Chen, Guangyi Nie, Xiaoyi Bai, Zhansheng Zhu, “Intelligent Prediction of Anti-risk Repayment Ability of Small and Medi-um-sized Enterprise Commercial Loan Data Based on Ant Colony Algorithm,” IEEE, pp. 1653–1656, 2021, doi: https://www.doi.org/10.1109/ICOEI51242.2021.9453015.
X. Xiao, Yi., Zhu, “Evaluation and Analysis of Anti Risk Repayment Ability Based on Business Loan Data of Small and Medium-Sized Enterprises,” vol. 2020.
S. Nemade, “Loan Repayment Ability Using Machine Learning,” Int. J. Sci. Technol. Eng., vol. 10, no. 7, pp. 4875–4882, 2022, doi: 10.22214/ijraset.2022.45683.
W. Wu, “Credit Risk Measurement, Decision Analysis, Transformation and Upgrading for Financial Big Data,” Complexity, vol. 2022, 2022, doi: 10.1155/2022/8942773.
V. A. Rakhaev, “Developing credit risk assessment methods to make loss provisions for potential loans,” Financ. Theory Pract., vol. 24, no. 6, pp. 82–91, 2020, doi: 10.26794/2587-5671-2020-24-6-82-91.
M. Sobarsyah, W. Soedarmono, W. S. A. Yudhi, I. Trinugroho, A. Warokka, and S. E. Pramono, “Loan growth, capitalization, and credit risk in Islamic banking,” Int. Econ., vol. 163, pp. 155–162, 2020, doi: 10.1016/j.inteco.2020.02.001.
M. Óskarsdóttir and C. Bravo, “Multilayer network analysis for improved credit risk prediction,” Omega (United Kingdom), vol. 105, 2021, doi: 10.1016/j.omega.2021.102520.
D. Li and L. Li, “Research on Efficiency in Credit Risk Prediction Using Logistic-SBM Model,” Wirel. Commun. Mob. Comput., vol. 2022, no. March 2005, 2022, doi: 10.1155/2022/5986295.
H. A. AL Qudah, “Credit risks measurement in Islamic banks study model,” J. Sustain. Financ. Invest., vol. 13, no. 1, pp. 210–228, 2023, doi: 10.1080/20430795.2021.1891783.
F. M. Assef and M. T. A. Steiner, “Ten-year evolution on credit risk research: A systematic literature review approach and discussion,” Ing. e Investig., vol. 40, no. 2, pp. 50–71, 2020, doi: 10.15446/ing.investig.v40n2.78649.
D. Albuquerque, A. I. Morais, and I. Pinto, “The role of banking supervision in credit risk disclosures and loan loss provisions,” Rev. Bras. Gest. Negocios, vol. 22, no. 4, pp. 932–948, 2020, doi: 10.7819/rbgn.v22i4.4078.
J. Wang, G. Liu, X. Xu, and X. Xing, “Credit risk prediction for small and medium enterprises utilizing adjacent enterprise data and a relational graph attention network,” J. Manag. Sci. Eng., vol. 9, no. 2, pp. 177–192, 2024, doi: 10.1016/j.jmse.2023.11.005.
F. B. G. Silva, Fiscal Deficits, Bank Credit Risk, and Loan-Loss Provisions, vol. 56, no. 5. 2021. doi: 10.1017/S0022109020000472.
G. Löffler, “The Systemic Risk Implications of Using Credit Ratings Versus Quantitative Measures to Limit Bond Portfolio Risk,” J. Financ. Serv. Res., vol. 58, no. 1, pp. 39–57, 2020, doi: 10.1007/s10693-019-00321-9.
T. Sukomardojo, A. K. Adisaputra, P. A. Cakranegara, M. A. Irawati, and T. Hendarto, “Economic Instability and Global Market Fluctuations in 2023: Analyzing Causes, Impacts, and Policy Responses,” Int. J. Sci. Soc., vol. 6, no. 1, pp. 799–809, 2024, doi: https://www.doi.org/10.54783/ijsoc.v6i1.1065.
H. Dorotheea, Beatrice, Ruxandra, Chiosea., Camelia-Daniela, “The impact of crisis situations on the financial indicators of romanian companies,” J. Financ. Stud., vol. 815, 2023, doi: https://www.doi.org/10.55654/jfs.2023.8.15.03.
O. Hemlata, Sharma., Aparna, Andhalkar., Oluwaseun, Ajao., B., “Analysing the Influence of Macroeconomic Factors on Credit Risk in the UK Banking Sector,” analytics, vol. 3, no. 1, pp. 63–83, 2024, doi: https://www.doi.org/10.3390/analytics3010005.
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