I. Gede Made Karma; I. Made Ariana; Desak Putu Suciwati

Digital Transformation of Credit Analysis at LPD Through Machine Learning Implementation

PDF (577KB), PP.142-154

Views: 0 Downloads: 0

Author(s)

I. Gede Made Karma ^1,* I. Made Ariana ¹ Desak Putu Suciwati ¹

1. Politeknik Negeri Bali, Bukit Jimbaran, Kuta Selatan, Badung, Bali-80364, Indonesia

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2026.04.09

Received: 10 Mar. 2026 / Revised: 30 Apr. 2026 / Accepted: 15 Jun. 2026 / Published: 8 Aug. 2026

Index Terms

LPD, Digital Transformation, Credit Analysis, Machine Learning, XGBoost Algorithm

Abstract

The Village Credit Institution (LPD) is a microfinance institution that plays a vital role in the rural economy in Bali. LPDs provide credit through a manual and subjective analysis process conducted by loan officers. This often hinders the objectivity and consistency of credit analysis. Modernization is a strategic step to address this issue. This study aims to analyze the digital transformation process in the LPD credit analysis system through the implementation of machine learning. Initial analysis indicates that debtors with long tenors, high delinquency rates, and high debt-to-income ratios have a higher risk of default. This pattern serves as the basis for learning a machine learning model using Logistic Regression, Decision Tree, Random Forest, and XGBoost algorithms to classify creditworthiness. The XGBoost algorithm demonstrated the best performance with an accuracy of 93% and an AUC of 0.96. Regarding credit approval, this model was able to identify high-risk potential debtors with significantly better accuracy than conventional methods. With the ability to process thousands of historical data points in a relatively short time, thereby accelerating decision-making, the application of machine learning significantly improves the efficiency and objectivity of credit analysis. This supports the realization of digital transformation in credit analysis at LPDs.

Cite This Paper

I. Gede Made Karma, I. Made Ariana, Desak Putu Suciwati, "Digital Transformation of Credit Analysis at LPD Through Machine Learning Implementation", International Journal of Information Technology and Computer Science(IJITCS), Vol.18, No.4, pp.142-154, 2026. DOI:10.5815/ijitcs.2026.04.09

Reference

[1]I. G. M. Karma, I. M. D. J. Sulastra, and N. W. K. Dewi, “A Health Level and Risk Rating Monitoring System of Village Credit Institutions,” International research journal of management, IT and social sciences, vol. 9, no. 6, pp. 788–799, Sep. 2022, doi: 10.21744/irjmis.v9n6.2167.
[2]I. R. Kumar, S. A. Hameed, B. Annapurna, R. K. Paladugu, V. S. N. Reddy, and K. K. Kaveti, “Modern machine learning and deep learning algorithms for preventing credit card frauds,” Indonesian Journal of Electrical Engineering and Computer Science, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusID:278399053
[3]R. Novaldi, “The Influence of Credit Risk on Financial Distress with Corporate Governance as a Moderating Variable,” Formosa Journal of Multidisciplinary Research, vol. 4, no. 9, pp. 4503–4520, Sep. 2025, doi: 10.55927/fjmr.v4i9.438.
[4]Y. Fang, “Research on Machine Learning in Credit Risk Management: Current Developments and Future Trends,” in International Conference on Artificial Intelligence and Machine Learning Research (CAIMLR 2024), A. Ghosh, H. Zhao, C. Wei Tan, and J. Liu, Eds., SPIE, May 2025, p. 13. doi: 10.1117/12.3058170.
[5]N. Shonhadji, “What Most Influence on Non-Performing Loan in Indonesia? Bank Accounting Perspective with MARS Analysis,” Journal of Accounting and Strategic Finance, vol. 3, no. 2, pp. 136–153, Nov. 2020, doi: 10.33005/jasf.v3i2.85.
[6]R. Adawiah, Heffi Christya Rahayu, and Makmur, “The Influence of Credit Risk Management and Market Risk on Financial Performance in Banking Companies,” Strata Business Review, vol. 3, no. 1, pp. 46–55, May 2025, doi: 10.59631/sbr.v3i1.346.
[7]N. Cheluhina and A. Gostev, “The Credit Risk Profile of a Commercial Bank: a Systematic Approach to Analysis and Management,” Scientific Research and Development. Economics of the Firm, vol. 14, no. 4, pp. 39–45, Dec. 2025, doi: 10.12737/2306-627X-2025-14-4-39-45.
[8]W. Yishan and T. Mingjie, “The Role of Corporate Financial Report Analysis in Credit Decision Making,” Lex localis - Journal of Local Self-Government, vol. 23, no. S6, pp. 3547–3557, Oct. 2025, doi: 10.52152/qbpa8681.
[9]D. Plekhanov, H. Franke, and T. H. Netland, “Digital Transformation: A Review and Research Agenda,” European Management Journal, vol. 41, no. 6, pp. 821–844, Dec. 2023, doi: 10.1016/j.emj.2022.09.007.
[10]H. Wen and N. H. Bajuri, “Digital Transformation: A Literature Review and Future Research Agenda,” International Journal of Academic Research in Business and Social Sciences, vol. 15, no. 2, Feb. 2025, doi: 10.6007/IJARBSS/v15-i2/24680.
[11]B. K. AlNuaimi, S. Kumar Singh, S. Ren, P. Budhwar, and D. Vorobyev, “Mastering Digital Transformation: The Nexus Between Leadership, Agility, and Digital Strategy,” J. Bus. Res., vol. 145, pp. 636–648, Jun. 2022, doi: 10.1016/j.jbusres.2022.03.038.
[12]J. Liu, “Analysis of the Credit System of Internet Consumer Finance Companies,” Exploring Science Academic Conference Series, vol. 10, pp. 297–304, Jan. 2026, doi: 10.70267/icbms.2502.297304.
[13]I. B. R. D. Mantra, I. G. B. Wiksuana, I. W. Ramantha, and N. L. P. Wiagustini, “The Role of Cultural Capital in Improving the Financial Performance of Village Credit Institutions,” Uncertain Supply Chain Management, vol. 11, no. 2, pp. 573–584, 2023, doi: 10.5267/j.uscm.2023.2.007.
[14]J. Susanti, I. G. M. Karma, and N. W. K. Dewi, “Design of Risk Rating Applications for Village Credit Institutions,” in International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021), Atlantis Press, 2022, pp. 633–639.
[15]I. G. M. Karma and N. K. Sukasih, “Designing Application for Determining the Health Level of Village Credit Institutions with the CAMEL Method,” in International Conference On Applied Sciece and Technology 2019 - Social Sciences Track (iCASTSS 2019), Atlantis Press, Oct. 2019. [Online]. Available: https://www.atlantis-press.com/article/125920667
[16]Putu Sri Arta Jaya Kusuma and Intan Maulyda, “Village Credit Institution: Does Business Risk Effect on Profitability?,” World Journal of Advanced Research and Reviews, vol. 20, no. 3, pp. 1066–1072, Dec. 2023, doi: 10.30574/wjarr.2023.20.3.2574.
[17]J. António Porfírio, J. Augusto Felício, and T. Carrilho, “Factors Affecting Digital Transformation in Banking,” J. Bus. Res., vol. 171, p. 114393, Jan. 2024, doi: 10.1016/j.jbusres.2023.114393.
[18]Abhishek Kumar, Abhijeet Kumar, Aditya Kumar Singh, and Ms. Nikita, “Credit Scoring System Using Machine Learning,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 10, no. 3, pp. 376–380, May 2024, doi: 10.32628/CSEIT2410334.
[19]F. Ferretti, “Peer-to-Peer Lending and EU Credit Laws: A Creditworthiness Assessment, Credit-Risk Analysis or … Neither of the Two?,” German Law Journal, vol. 22, no. 1, pp. 102–121, Jan. 2021, doi: 10.1017/glj.2020.100.
[20]U. Adams, “Digital Transformation and Its Impact on Business Models,” International Journal of Economics, Business and Management Research, vol. 09, no. 02, pp. 132–142, 2025, doi: 10.51505/IJEBMR.2025.9212.
[21]R. Y. Choi, A. S. Coyner, J. Kalpathy-Cramer, M. F. Chiang, and J. Peter Campbell, “Introduction to machine learning, neural networks, and deep learning,” Transl. Vis. Sci. Technol., vol. 9, no. 2, 2020, doi: 10.1167/tvst.9.2.14.
[22]Gargi Fadtare, Antarisksha Dhanure, Gayatri Gunjal, Krutika Kolpate, and Nilesh Bhelkar, “Study on Predictive Modeling for Loan Approval,” International Research Journal on Advanced Engineering and Management (IRJAEM), vol. 2, no. 10, pp. 3160–3165, Oct. 2024, doi: 10.47392/IRJAEM.2024.0466.
[23]A. S and D. K. S, “A Comparison of Various Machine Learning Algorithms and Deep Learning Algorithms for Prediction of Loan Eligibility,” Int. J. Res. Appl. Sci. Eng. Technol., vol. 11, no. 6, pp. 4558–4564, Jun. 2023, doi: 10.22214/ijraset.2023.54495.
[24]A. Haque, Md. Mahedi, and H. Lecturer, “Bank Loan Prediction Using Machine Learning Techniques,” ArXiv, vol. abs/2410.08886, 2024, [Online]. Available: https://api.semanticscholar.org/CorpusID:273323849
[25]E. L. Campos and R. P. Cysne, “Estimating Debt Limits for Emerging Countries,” International Review of Economics & Finance, vol. 76, pp. 836–855, Nov. 2021, doi: 10.1016/j.iref.2021.07.014.
[26]D. et al., “Determinants of Credit Risk at Vietnam Bank for Agriculture and Rural Developments in Can Tho City,” International Journal of ADVANCED AND APPLIED SCIENCES, vol. 9, no. 2, pp. 31–40, Feb. 2022, doi: 10.21833/ijaas.2022.02.004.
[27]T. Mokheleli and T. Museba, “Machine Learning Approach for Credit Score Predictions,” Journal of Information Systems and Informatics, vol. 5, no. 2, pp. 497–517, May 2023, doi: 10.51519/journalisi.v5i2.487.
[28]Y. Sun, “Effect of Credit Card Holders’ Age on the Credit Limit,” Highlights in Business, Economics and Management, vol. 7, pp. 150–155, Apr. 2023, doi: 10.54097/hbem.v7i.6936.
[29]G. Caruso, S. A. Gattone, F. Fortuna, and T. Di Battista, “Cluster Analysis for Mixed Data: An Application to Credit Risk Evaluation,” Socioecon. Plann. Sci., vol. 73, p. 100850, Feb. 2021, doi: 10.1016/j.seps.2020.100850.
[30]P. Di Biasi, R. Gnutti, A. Resti, and D. Vergari, “Machine Learning for Credit Risk: Three Successful Case Histories,” Risk Management Magazine, vol. 17, no. 2, pp. 5–18, Aug. 2022, doi: 10.47473/2020rmm0108.
[31]S. Liu, “Application Analysis of Machine Learning Models in Credit Risk,” Highlights in Science, Engineering and Technology, vol. 107, pp. 76–81, Aug. 2024, doi: 10.54097/6qm0dk77.
[32]Gupta Ashwin Arunkumar, Chaurasiya Ravi Panchuram, Khambati Mohammed Aadil Afzal, Niraj Sureshchand Yadav, and Uma Goradiya, “Predictive Analysis in Banking using Machine Learning,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology, pp. 434–439, Apr. 2023, doi: 10.32628/CSEIT2390247.
[33]G. Sam, P. Asuquo, and B. Stephen, “Customer Churn Prediction using Machine Learning Models,” Journal of Engineering Research and Reports, vol. 26, no. 2, pp. 181–193, Feb. 2024, doi: 10.9734/jerr/2024/v26i21081.
[34]T. A. Acharya and P. V. Upasan, “Credit Risk Assessment - A Machine Learning Approach,” in Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 471 LNICST, 2023, pp. 39–54. doi: 10.1007/978-3-031-35081-8_4.
[35]F. T. Araya, B. G. Assefa, S. K. Mohapatra, and C. B. Nanda, “Sustainable Credit Risk Prediction: Applying Machine Learning for Responsible Lending,” Journal of Transformative Technologies and Sustainable Development, vol. 9, no. 1, p. 12, Oct. 2025, doi: 10.1007/s41314-025-00080-7.
[36]I. Aruleba and Y. Sun, “An Improved Ensemble Method With Data Resampling for Credit Risk Prediction,” IEEE Access, vol. 13, pp. 71275–71287, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusID:278084184
[37]R. Sarkar, “A Systematic Review of AI-Driven Credit Risk Assessment Models in Commercial Banking (2018–2026),” American Journal of Interdisciplinary Studies, vol. 07, no. 01, pp. 459–495, Jan. 2026, doi: 10.63125/m52yna23.
[38]K. Dutia, “Financial Risk Assessment for Loan Application using Logistic Regression and Factor Analysis,” International Journal For Multidisciplinary Research, vol. 7, no. 5, Sep. 2025, doi: 10.36948/ijfmr.2025.v07i05.55787.
[39]E. Chavalala and L. T. Khoza, “Modeling the Probability of Bank Loan Eligibility Using Machine Learning Model,” 2025, pp. 767–793. doi: 10.1007/978-981-96-9697-0_59.
[40]J. Alvi, I. Arif, and K. Nizam, “Advancing Financial Resilience: A Systematic Review of Default Prediction Models and Future Directions in Credit Risk Management,” Heliyon, vol. 10, no. 21, p. e39770, Nov. 2024, doi: 10.1016/j.heliyon.2024.e39770.
[41]Baidyanath Sou, “Machine Learning for Credit Scoring Evaluation: a Survey,” International Journal For Multidisciplinary Research, vol. 2, no. 5, Oct. 2020, doi: 10.36948/ijfmr.2020.v02i05.10691.
[42]Q. G. Nguyen et al., “Enhancing Credit Risk Management with Machine Learning: A Comparative Study of Predictive Models for Credit Default Prediction,” The American Journal of Applied Sciences, vol. 07, no. 01, pp. 21–30, Jan. 2025, doi: 10.37547/tajas/Volume07Issue01-04.
[43]S. Li, “Comparative Analysis of Ensemble Learning Models for Credit Scoring: Challenges, Improvements, and Explainability,” in 2025 IEEE 4th International Conference on Computing and Machine Intelligence (ICMI), IEEE, Apr. 2025, pp. 1–5. doi: 10.1109/ICMI65310.2025.11141244.
[44]D. Li and S. Xu, “Research on the Path of Enterprise Management Innovation Based on Multiple Logistic Regression Model,” Applied Mathematics and Nonlinear Sciences, vol. 9, no. 1, Jan. 2024, doi: 10.2478/amns.2023.2.00065.
[45]P. Della Corte, A. Jeanneret, and E. D. S. Patelli, “A Credit-Based Theory of the Currency Risk Premium,” J. financ. econ., vol. 149, no. 3, pp. 473–496, Sep. 2023, doi: 10.1016/j.jfineco.2023.06.002.
[46]J. S. Manyama, H. J. Haule, and H. H. Selemani, “Effectiveness of Credit Risk Management in Reducing Non-Performing Loans of Commercial Banks in Tanzania,” East African Journal of Business and Economics, vol. 9, no. 1, pp. 45–61, Jan. 2026, doi: 10.37284/eajbe.9.1.4301.
[47]R. I. Pratiwi, A. Mas’ud, S. Saleh, and W. Hamid, “The Role of Profitability and Liquidity Risk in Mediating the Effect of Credit Risk on Stability in Regional Development Banks in Indonesia,” Architecture Image Studies, vol. 7, no. 1, pp. 498–509, Jan. 2026, doi: 10.62754/ais.v7i1.867.
[48]B. - and R. Bhavisetti, “Credit Risk Management, Strategic Lending Decisions and Bank Profitability,” International Journal For Multidisciplinary Research, vol. 8, no. 1, Jan. 2026, doi: 10.36948/ijfmr.2026.v08i01.66491.
[49]C. Rauly Simatupang, M. Muslimin, M. Y. Kasim, and F. Fera, “The Effect of Credit Risk on Profitability of Indonesian State-Owned Commercial Banks: The Intervening Role of Operating Efficiency,” Eduvest - Journal of Universal Studies, vol. 6, no. 1, pp. 987–995, Jan. 2026, doi: 10.59188/eduvest.v6i1.52660.
[50]Putri Handayani, “Credit Risk Management and Non-Performing Loan Dynamics in Home Ownership Lending: Evidence from an Indonesian State-Owned Bank,” International Journal of Economics, Education, Law and Social Sciences (IJEELSC), vol. 2, no. 1, pp. 48–56, Jan. 2026, doi: 10.61990/awnd7h93.
[51]I. Ismiantika, R. W. D. Paramita, and I. Fadah, “The Role of Risk Management on the Effect of Asset Quality and Sustainability on Banking Financial Distress,” Assets : Jurnal Ilmiah Ilmu Akuntansi, Keuangan dan Pajak, vol. 10, no. 1, pp. 61–78, Jan. 2026, doi: 10.30741/assets.v10i1.1893.

International Journal of Information Technology and Computer Science (IJITCS)