Leveraging Business Intelligence And Big Data For Financial Risk Management Within The Supply Chain.

Abstract

Financial risk management and the identification of risks have become integral elements of global supply networks. Employing machine learning, big data, and business intelligence technologies aids in detecting systemic risks, managing financial uncertainties, and pinpointing the root causes of risks. Companies may actively work to enhance the origins of these risks. The methodology focuses on three primary areas of supply chain risk: shipping, marketing, and distribution. A proprietary insurance model is constructed, assessed, and aligned with current practices before being adjusted based on outcomes. Additionally, Business Intelligence (BI) is employed to enable businesses to make informed, data-driven decisions and consequently reduce incurred losses. The framework incorporates risk detection models grounded in machine learning, business intelligence, and big data to govern financial risks in the supply chain. This study endeavors to identify, trace the sources of, and mitigate risks. The proposed approach specifically targets three key supply chain risk areas: transportation, sales, and delivery. A risk disclosure model is implemented, tested, compared to existing methodologies, and refined based on outcomes in each area. Regardless of the utilized supply chain data, the suggested method proves adaptable to various supply chain types.