Assessing Financial Distress: “A Comparative Analysis of Altman Z-Score and Machine Learning Models Within a Technology Adoption Framework”

Financial distress is an unfavourable situation that a company may experience at any point in its lifetime. All the company stakeholders are interested in its financial stability since they have different kinds of interests. Consequently, it is management’s responsibility to take all the necessary measures to prevent financial distress in any company. This study assesses the financial distress of the top five companies by market capitalisation in the automobile industry listed in the NSE and operating in India for a period of seven years from 2019 to 2025 using the Altman Z-score model. The same data set was calculated using the Altman Z, Score test with ML, based on tools such as Python, using Artificial Intelligence, by applying the same variables to compare the result accuracy. The present analysis technology aims to illuminate how technological tools supplement traditional methods. By integrating traditional and technological models, this research demonstrates the relevance of both models, and these findings assist managers in selecting tools that are appropriate for their strategic goals and implementation. The study found that comparing the trends in the data and the AI-generated computations for financial decisions revealed consistent trends, but the results calculated by Python were generally more accurate, easier to predict, and more useful for financial decision-making. AI-generated results, unlike static ones, are mathematically superior and linked with the technological implementation.