TO LEVERAGE FINANCIAL DATA OF DIFFERENT VENDORS OF FMCG MNCS (KEY IJP************015)

• Navya Sengar

Abstract

In the rapidly evolving landscape of fast-moving consumer goods (FMCG), multinational corporations (MNCs) such as Procter & Gamble, Hindustan Unilever, Amul, and Coca-Cola face significant challenges in managing vendor relationships and assessing credit risks effectively. This paper aims to leverage the financial data from various vendors to analyze credit risk utilizing advanced artificial intelligence (AI) and machine learning (ML) methodologies. We explore a comprehensive dataset that includes critical financial indicators like balance sheets, income tax returns, order books, plant setups, and the availability of skilled manpower. Furthermore, we incorporate operational data, qualitative assessments, and product-specific information to holistically evaluate vendor stability and predict potential credit risks. The research framework involves integrating diverse data sources to develop a robust risk assessment model. Using machine learning algorithms, we analyze patterns and relationships within the data that may signify the likelihood of vendor defaults or financial distress. This predictive modeling approach enables MNCs to make informed decisions when engaging with suppliers, thus minimizing potential financial losses. Moreover, we discuss the implications of AI and ML in automating risk assessment processes, leading to more efficient vendor management strategies that can be adopted across the FMCG sector. Our findings illuminate significant predictors of credit risk, providing actionable insights for MNCs to enhance their vendor selection processes while ensuring financial resiliency. The application of these advanced technologies not only streamlines risk management but also equips corporations with the analytical capacity to adapt and thrive in an increasingly competitive environment.