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Measuring The Financial Health of Companies Using Z-Score and Data Analytics
Subject area: Management and Commerce · Area of research: Financial Management, Business Analytics
DOI: 10.64388/IREV9I3-1710676-4722
Abstract
The study examines the financial health of MCKB Construction LLP, Bengaluru, using Altman?s Z-Score model over a five-year period (2020?2024). The research applies ratio analysis combined with data analytics tools to classify the company?s solvency status into Safe, Grey, or Distress zones. Findings reveal that while the firm demonstrated robust financial health in the early years, it experienced a steady decline, entering the Grey Zone in 2023?2024. Persistent liquidity shortages, weakening asset turnover, and declining operational efficiency are key concerns. The study concludes with recommendations for strengthening working capital, restructuring debt, and adopting predictive analytics for proactive financial monitoring.
Keywords
Financial Health, Altman?s Z-Score, Bankruptcy Prediction, Data Analytics, Construction Industry, Working Capital Management, Solvency and Liquidity, Predictive Financial Models, Risk Assessment.
References
[1] Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4(Empirical Research in Accounting: Selected Studies 1966), 71–111. https://doi.org/10.2307/2490171
[2] Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2326758
[3] Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109–131. https://doi.org/10.2307/2490395
[4] Platt, H., & Platt, M. (1990). Development of a class of stable predictive variables: The case of bankruptcy prediction. Journal of Business Finance & Accounting, 17(1), 31–51. https://doi.org/10.1111/j.1468-5957.1990.tb00548.x
[5] Bellovary, J., Giacomino, D., & Akers, M. (2007). A review of bankruptcy prediction studies: 1930–present. Journal of Business & Economics Research, 5(2), 7–14. https://doi.org/10.19030/jber.v5i2.2532
[6] Yoon, J., & Kwon, Y. (2020). Predicting financial distress with machine learning: Evidence from SMEs. Sustainability, 12(19), 7865. https://doi.org/10.3390/su12197865
[7] World Bank. (2024). Finance & Prosperity 2024. World Bank Publications. Retrieved from https://www.worldbank.org/en/publication/finance-and-prosperity-2024
How to cite this paper
@article{1710676,
author = {Shreenidhi S, Prathyusha Pasupuleti},
title = {Measuring The Financial Health of Companies Using Z-Score and Data Analytics},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {3},
pages = {1985-1988},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1710676.pdf},
abstract = {The study examines the financial health of MCKB Construction LLP, Bengaluru, using Altman?s Z-Score model over a five-year period (2020?2024). The research applies ratio analysis combined with data analytics tools to classify the company?s solvency status into Safe, Grey, or Distress zones. Findings reveal that while the firm demonstrated robust financial health in the early years, it experienced a steady decline, entering the Grey Zone in 2023?2024. Persistent liquidity shortages, weakening asset turnover, and declining operational efficiency are key concerns. The study concludes with recommendations for strengthening working capital, restructuring debt, and adopting predictive analytics for proactive financial monitoring.},
keywords = {Financial Health, Altman?s Z-Score, Bankruptcy Prediction, Data Analytics, Construction Industry, Working Capital Management, Solvency and Liquidity, Predictive Financial Models, Risk Assessment.},
month = {September},
doi = {https://doi.org/10.64388/IREV9I3-1710676-4722}
}