Evaluating The Influence Of AI-Driven Predictive Analytics on Strategic Financial Decisions in the Commerce Sector
DOI:
https://doi.org/10.64882/ijrt.v13.iS4.841Keywords:
AI, predictive analytics, financial decision-making, commerce, risk management, explainability, secondary dataAbstract
This paper examines how AI-driven predictive analytics is reshaping strategic financial decision-making within the commerce sector. Using a secondary-data approach (industry reports, peer-reviewed articles, consultancy white papers, and reputable news coverage), the study synthesizes evidence on value creation (forecasting accuracy, risk management, customer lifetime value, pricing and inventory optimization), organizational enablers, and major challenges (bias, explainability, governance, regulation). Key findings show that organizations that combine strategic leadership, high-quality data, and disciplined model-validation practices capture disproportionate financial benefits from predictive analytics, while failures in transparency and governance amplify legal and reputational risks. The paper ends with practical recommendations for managers and policymakers to realize AI value while managing risks.
References
McKinsey & Company. The State of AI: Global Survey 2025 (overview and findings on AI adoption and practices).
McKinsey & Company. The state of AI in 2021 (Global Survey).
McKinsey & Company. Building the AI bank of the future (AI in financial services examples and implications.
Broby, D. (2022). The use of predictive analytics in finance — Sciencedirect/peer-reviewed article on forecasting methods in finance.
ResearchGate / Predictive Analytics articles and case studies (various): predictive analytics in financial management and CLV case studies.
Reuters. Legal transparency in AI finance: facing the accountability dilemma in digital decision-making (analysis of regulatory and legal challenges in AI finance).
Ferrara, E., et al. (2023). Fairness And Bias in Artificial Intelligence (arXiv survey on bias sources and mitigations).
Downloads
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.




