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This study aims to evaluate the effectiveness of AI in enhancing financial modeling for sustainable business decisions.

June 3, 2026Open Access

An Empirical Study of AI-Driven Financial Modelling for Sustainable Business Decision-Making

Key Points

This study aims to evaluate the effectiveness of AI in enhancing financial modeling for sustainable business decisions.
Utilized ten years of secondary financial data from Tata Motors (FY2013–FY2022).
Employed AI techniques like Linear Regression, Monte Carlo-inspired Scenario Simulation, and Z-Score Benchmarking.
Conducted Income Statement Analysis, DCF Valuation, and Financial Ratio Analysis for comprehensive assessments.
AI-based forecasting improves revenue prediction accuracy by 41% versus traditional methods.
Scenario-driven DCF analysis indicates a broader valuation range for better risk assessment.
AI benchmarking identifies critical financial outliers and competitive advantages within Tata Motors' operations.

Abstract

An Empirical Study of AI-Driven Financial Modelling for Sustainable Business Decision-Making Girish B. Sarwadkar Research Scholar, BV (DU) Pune, Abhijit Kadam Institute of Management and Social Sciences, Solapur Rohan G. Chavan Research Scholar, BV (DU) Pune, Abhijit Kadam Institute of Management and Social Sciences, Solapur Dr. Shivganga C. Maindargi Assistant Professor, BV (DU) Pune, Abhijit Kadam Institute of Management and Social Sciences, Solapur Abstract Artificial Intelligence (AI) is transforming financial management by enabling intelligent, data-driven, and sustainability-oriented decision-making processes. Traditional financial models often rely on static assumptions and manual analysis, limiting their ability to address dynamic business environments and sustainability considerations. This study examines the effectiveness of AI-augmented financial modeling in enhancing business decisions through Income Statement Analysis, Discounted Cash Flow (DCF) Valuation, and Financial Ratio Analysis. The research utilizes ten years of secondary financial data of Tata Motors Limited (FY2013–FY2022). AI techniques including Linear Regression, Monte Carlo-inspired Scenario Simulation, and Z-Score Benchmarking are employed to improve forecasting accuracy, valuation assessment, and anomaly detection. The findings indicate that AI-based forecasting improves revenue prediction accuracy by 41% compared to traditional methods. Scenario-driven DCF analysis reveals a broader valuation range, enabling more comprehensive risk assessment and strategic planning. Furthermore, AI benchmarking identifies critical financial outliers and competitive advantages within the firm’s operating structure. The study highlights the role of AI-driven financial modeling in supporting ESG-oriented decision-making and sustainable business transformation, particularly in facilitating Tata Motors’ transition toward electric vehicle (EV) innovation and long-term value creation. Keywords: AI Financial Modeling, Tata Motors, DCF Valuation, Ratio Analysis, Sustainable Decision-Making, Z-Score Benchmarking, ESG, Indian Automotive Sector

An Empirical Study of AI-Driven Financial Modelling for Sustainable Business Decision-Making

Key Points

Abstract

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