INTEGRATING AHP IN BIG DATA RISK MANAGEMENT FOR FINANCIAL INSTITUTIONS: A SYSTEMATIC APPROACH
DOI:
10.54443/morfai.v5i3.3991Published:
2025-09-20Downloads
Abstract
The big data revolution has reshaped risk management paradigms in the financial sector while introducing complex, dynamic, and multidimensional risk challenges. This study regularly examines the integration of the Analytic Hierarchy Process (AHP) into big data risk management for financial institutions through a Systematic Literature Review (SLR) using the PRISMA protocol, covering publications from the past decade. Findings indicate that AHP—in both classical and modified forms such as Fuzzy AHP and AHP-DEA—effectively structures hierarchical risk frameworks that integrate quantitative criteria (probability, financial impact) and qualitative aspects (reputation, compliance). Big data integration enriches the weighting process with real-time data from internal sources, markets, and public sentiment, thereby reducing subjective bias and enhancing decision reliability. This approach enables adaptive risk prioritization in response to market and regulatory changes, overcoming the limitations of static AHP models and supporting more holistic, measurable risk mitigation. The results underscore that the AHP–big data framework offers financial institutions a competitive advantage through rapid, evidence-based, objective, and sustainability-oriented decision-making.
Keywords:
AHP, big data, risk management, financial institutions, decision-makingReferences
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