Credit Scoring Through Mathematical Modeling: Applying the Sherrod Approach to Solaria Tech (Morocco, 2023)

Author's Information:

Saoud Ikram

Master's student in Participatory Finance Engineering and Artificial intelligence, Faculty of Legal, Economic, and Social Sciences - Ain Sbaa, University Hassan II Casablanca, Morocco.

Benarafah Zineb

Master's student in Participatory Finance Engineering and Artificial intelligence, Faculty of Legal, Economic, and Social Sciences - Ain Sbaa, University Hassan II Casablanca, Morocco.

El Mekaoui Marwa

Master's student in Participatory Finance Engineering and Artificial intelligence, Faculty of Legal, Economic, and Social Sciences - Ain Sbaa, University Hassan II Casablanca, Morocco.

N’quila Zineb

Master's student in Participatory Finance Engineering and Artificial intelligence, Faculty of Legal, Economic, and Social Sciences - Ain Sbaa, University Hassan II Casablanca, Morocco.

Sghir Salma

Master's student in Participatory Finance Engineering and Artificial intelligence, Faculty of Legal, Economic, and Social Sciences - Ain Sbaa, University Hassan II Casablanca, Morocco.

Balla Nouhayla

Master's student in Participatory Finance Engineering and Artificial intelligence, Faculty of Legal, Economic, and Social Sciences - Ain Sbaa, University Hassan II Casablanca, Morocco.

Faris Asmaa

Laboratory of Applied Modeling for Economics and Management, Faculty of Legal, Economic, and Social Sciences - Ain Sbaa, University Hassan II Casablanca, Morocco.

Elhachloufi Mostafa

Department of Statistics and Applied Mathematics for Economics and Management, University Hassan II Casablanca, Morocco.

Vol 02 No 09 (2025):Volume 02 Issue 09 September 2025

Page No.: 913-924

Abstract:

This study aims to analyze and assess the credit risk of Moroccan solar energy solutions company Solaria Tech, by applying the Sherrod mathematical model. 

An analytical descriptive approach based on digital simulation and real financial statements of the company for the year 2023, including the budget and results calculation tables, was adopted. The study used six key financial indicators (X1) to (X6) that represent the basic ratios adopted in the Sherrod model, such as the ratio of working capital, the ratio of liquid assets, net profit to assets, and others.

The results of the analysis showed that the calculated value of the Z coefficient was 8.28, which indicates that the company falls into the third category according to the bank's classification, that is, it represents an average degree of credit risk, and the study also showed the company's good ability to generate profits and finance its fixed assets through equity, which enhances its credit confidence despite some limited liquidity indicators.

The study concludes that it is important to use quantitative models such as Sherrod to support loan decisions and reduce financial risks, especially in vital sectors such as renewable energy.

KeyWords:

credit risk, Sherrod model, mathematical programming, financial analysis, solaria tech

References:

  1. AL-Hmadane, S. T. (2023). Using Sherrod Model Indicators to Predict Financial Failure.
  2. Al-Kanani. (2022). Investment decision evaluation. Baghdad: Dar Al-Doctor.
  3. Al-Safwani. (2020). The possibility of using the KIDA model for financial failure prediction. Academic Research Journal., Issue 26.
  4. Altman, E. (1977). Anlusis:A New Modal to identify bankruptcy risk of corporation. journal of banking & finance.
  5. Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance.
  6. Amer, A. H. (2023). Using the CAMELS system in analyzing capital adequacy, profitability and liquidity. Al-Ghari Journal for Economic and Administrative Sciences. 
  7. Anderson, R. (2007). The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation (éd. août 2007 (1ʳᵉ édition)). (O. U. Press, Éd.) Récupéré sur TheCreditScoringToolkitTheoryAndPracticeForRetailCreditRiskMgmt.DecisionAutomati
  8. Anderson, R. (août 2007 (1ʳᵉ édition)). The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation (éd. (1ʳᵉ édition) ). Récupéré sur TheCreditScoringToolkitTheoryAndPracticeForRetailCreditRiskMgmt.DecisionAutomati
  9. Bessis, J. (2015). Risk Management in Banking. (éd. 4ᵉ édition). (W. Finance, Éd.) Récupéré sur https://www.wiley.com/en-us/Risk+Management+in+Banking%2C+4th+Edition-p-9781118660218
  10. Dridi, M. H. (2011). The Effects of the Global Crisis on Islamic and Conventional Banks: A Comparative Study. Récupéré sur https://www.imf.org/external/pubs/ft/wp/2010/wp10201.pdf
  11. Gerald I. White, A. C. (2003). The Analysis and Use of Financial Statements. (Wiley, Éd.) 784. Récupéré sur https://www.scribd.com/document/487907191/Gerald White Ashwinpaul C Sondhi Haim D Fried The Analysis and Use of Financial-Statements-Wiley-2002-pdf
  12. hassan, D. (2022). Effectiveness of Banking Information Systems in Managing Credit Failure Cases. Ouargla: Master's thesis.
  13. Higgins, R. (2012). Analysis for financial management (éd. 10ᵉ édition). (i. a. McGraw‑Hill/Irwin (série « The McGraw‑Hill/Irwin series in finance, Éd.) Récupéré sur https://students.aiu.edu/submissions/profiles/resources/onlineBook/i5a6J5_Analysis_for_Financial_Management_10th.pdf?utm
  14. Hull, J. C. (2018). Risk Management and Financial Institutions. (éd. 5ᵉ édition). (W. Finance, Éd.) Récupéré sur https://www.simonfoucher.com/MBA/FINA%20695%20-%20Risk%20Management/riskmanagementandfinancialinstitutions4theditionjohnhull-150518225205-lva1-app6892.pdf
  15. Krishna G. Palepu, P. M. (2008). Business Analysis and Valuation: Using Financial Statements (éd. 4ᵉ édition ). (C. L. Learning), Éd.) Récupéré sur https://students.aiu.edu/submissions/profiles/resources/onlineBook/L5Y5h8 Business Analysis Valuation Text and Cases Third IFRS Edition.pdf
  16. Lieberman, H. (s.d.). Finance & Financial Services. 2015. Récupéré sur https://www.traublieberman.com/practices/finance-financial-services
  17. Lutfi. (2005). Financial analysis for performance evaluation and review and investment in the stock exchange. Alexandria. 
  18. M. Mokhtar, A. S. (2014). Mathematical Programming Models for Portfolio Optimization Problem. World Academy of Science, Engineering and Technology International Journal of Mathematical and Computational Sciences. Récupéré sur https://www.academia.edu/98433210/Mathematical_Programming Models For Portfolio Optimization Problem A Review
  19. sharife, A. (2022). "Financial Failure in Economic Enterprises: From Diagnosis to Prediction and Treatment". National Symposium, Mentouri University of Constantine. 
  20. Smith, M. (1993). Neural Networks for Statistical Modeling. (V. N. Reinhold, Éd.) Récupéré sur https://archive.org/details/neuralnetworksfo0000smit
  21. wajihad, m. (2022). An-Najah National University for Research.
  22. Yahya, Y. &.-Z. (2006). "Problem Credit Facilities in Palestinian Banks. gaza: Master's thesis.
  23. Yassin, S. T.-H. (2024). Yassin, S. T., & Al-استخدام مؤشرات نموذج شيرود للتنبؤ بالفشل المالي: بحث تطبيقي في الشركة الوطنية للاستثمار السياحي في العراق للفترة. مجلة الدراسات الكردية.
  24. الشريف. (2022). الفشل المالي في المؤسسة الاقتصادية: من التشخيص إلى التنبؤ ثم العلاج. ملتقى وطني، جامعة منتوري قسنطينة. 
  25. الصفواني. (2020). مكانية استخدام نموذج KIDA للتنبؤ بالفشل المالي. مجلة البحوث الأكاديمية, العدد 26.
  26. الكناني. (2022). تقييم قرارات الاستثمار. بغداد: دار الدكتور.
  27. ديب, ح. (2022). فعالية نظم المعلومات المصرفية في تسيير حالات فشل الائتمان. ورقلة: رسالة ماجستير.
  28. عامر, أ. ه. (2023). استخدام نظام CAMELS في تحليل كفاية رأس المال والربحية والسيولة. مجلة الغري للعلوم الاقتصادية والإدارية.
  29. لطفي. (2005). التحليل المالي لأغراض تقييم ومراجعة الأداء والاستثمار في البورصة. الاسكندرية.
  30. وجهاد, م. (2022). جامعة النجاح للابحاث, 52.