Extending and Improving Current Frameworks for Risk Management and Decision Making: Incorporating Dynamic Aspects of Risk and Uncertainty in Saudi Arabia

Extending and Improving Current Frameworks for Risk Management and Decision Making: Incorporating Dynamic Aspects of Risk and Uncertainty in Saudi Arabia

Ezzat, Amr

Almohandseen, Giza, Egypt

DOI: https://dx.doi.org/10.47772/IJRISS.2025.914MG00139

Received: 03 August 2025; Accepted: 09 August 2025; Published: 08 September 2025

ABSTRACT

An improved dynamic risk management approach designed for Saudi Arabia’s quickly changing legislative and economic environment is put forth in this article. Adaptive and forward-looking risk measures are more important than ever in light of Vision 2030’s lofty ambitions, especially the push for infrastructure megaprojects, privatization, and economic diversification. Current risk management methods, which are frequently linear and static, are inadequate in VUCA (volatility, uncertainty, complexity, and ambiguity) contexts. We address this by presenting a new integrative framework that combines AI-based predictive analytics, fuzzy logic, multi-criteria decision-making (MCDM), and real-time monitoring systems. The framework strengthens the robustness of strategic decision-making, increases stakeholder response, and promotes ongoing review by including these dynamic tools. The framework’s applicability and policy consequences are illustrated by two case studies, one of which concerns the national COVID-19 reaction and the other a NEOM infrastructure program. Significant gains in agility, coordination, and outcome quality are demonstrated by the results, indicating that this strategy may find wider use in other Gulf Cooperation Council (GCC) countries.

Keywords: Vision 2030, decision-making in the face of uncertainty, dynamic risk management, fuzzy reasoning, Saudi Arabia, real-time monitoring, and multi-criteria analysis.

JEL Subject Codes: G32, D81, O53

INTRODUCTION

In both the public and private sectors, strategic planning is based on effective risk management. Traditional risk frameworks have been put to the test in the twenty-first century due to the frequency and severity of disruptions, which range from pandemics and geopolitical shocks to technological malfunctions and climate-related catastrophes. These frameworks, which are usually reactive and linear in nature, are becoming less and less appropriate for handling the complexity of contemporary decision-making contexts, especially in developing nations that are rapidly changing.

One country dealing with this crucial turning point is Saudi Arabia, which has the biggest economy in the Middle East and North Africa (MENA) region. With investments in tourism, technology, infrastructure, renewable energy, and human capital development, the Kingdom’s ambitious Vision 2030 reform plan aims to diversify its economy and break away from its historically oil-dependent economic model. Conventional risk management approaches are unable to fully capture the new and dynamic layers of risk brought about by this historic transformation.

For example, megaprojects such as NEOM, the Red Sea Project, and Qiddiya involve large-scale capital investments with intricate supply chains and multifaceted stakeholder ecosystems. Similarly, policy reforms such as the privatization of health and education services introduce regulatory and operational uncertainties that evolve over time. The COVID-19 pandemic further highlighted the limitations of static risk frameworks, as many decision-making bodies struggled to respond with agility to rapidly shifting public health, logistical, and socio-economic conditions.

In light of this, a paradigm change in the conceptualization and operationalization of risk and uncertainty in the Saudi context is becoming increasingly necessary. This essay makes the case that dynamic components that enable ongoing sensing, real-time feedback, and adaptive decision-making must be incorporated into present risk management systems. This study suggests a new integrative framework that can more effectively match risk management procedures with the breadth and speed of socioeconomic change in Saudi Arabia. It does this by utilizing techniques like fuzzy logic, artificial intelligence, and multi-criteria decision analysis (MCDA).

The goal is to increase resilience, stakeholder confidence, and long-term sustainability in addition to decision-making efficiency. Saudi Arabia can better predict and address new dangers by integrating this dynamic approach into its institutional and governance frameworks, protecting its economic and developmental goals in the process.

REVIEW OF LITERATURE

Static Risk of Frameworks and their Limitations

The linear cycles of risk identification, analysis, response planning, and monitoring characterize traditional risk management frameworks like ISO 31000 and COSO ERM. These models use the assumption that there is a stable environment in which risks can be reasonably predicted, classified, and managed (Hillson, 2009). But these models are coming under more and more fire for failing to account for nonlinear disturbances and new dangers (Paltrinieri et al., 2014).

Risk risks are constantly changing across the geopolitical, regulatory, and technical spheres, especially in unstable and complicated environments like Saudi Arabia. For example, early signs of supply chain instability or public sector reforms that alter investment incentives overnight could go unnoticed by static models. According to academics, these frameworks are fundamentally outdated since they rely on facts from the past, which may not be applicable in situations that are changing quickly. (Aven & Renn, 2010)

The shift toward Dynamic Risk Management

Dynamic risk management has become increasingly popular as a response to the shortcomings of static techniques. Continuous data flows, feedback systems, and adaptive learning capabilities are all integrated into dynamic frameworks. The Dynamic Risk Management Framework (DRMF), which was presented by Paltrinieri et al. (2013), uses real-time reassessment and scenario-based analysis to control risks in offshore oil and gas operations. Similar techniques for dynamic risk visualization utilizing neural networks and system-theoretic models were presented by Khan and Abbassi (2016).

These models process enormous volumes of data and identify subtle signs of change by utilizing digital transformation and AI-powered technology. Neural network models have been utilized in the Middle East to provide dynamic operational risk assessments (DORA), providing early warning signs based on real-time financial and environmental inputs, according to Sarbayev et al. (2019).

Applications in Saudi Context

Regional adaptation is crucial, even though worldwide research provide a solid basis. For dynamic risk management, Saudi Arabia’s institutional, legal, and cultural contexts offer special opportunities as well as obstacles. Ahmed et al. (2021) used Monte Carlo simulations and fuzzy logic to evaluate risk under uncertainty in the Saudi construction industry, showing that hybrid approaches provide higher accuracy in high-uncertainty situations.

Risk exposure has increased as a result of Saudi Arabia’s Vision 2030 initiative, which has sped up changes to infrastructure development, foreign investment policies, and regulatory frameworks. For instance, there are reputational and institutional risks associated with the privatization of important public services, and these risks change according to societal and political attitude. According to Hammad (2024), Saudi businesses need flexible governance systems in order to comply with regulations.

Furthermore, the multinational alliances that Saudi megaprojects like NEOM rely on introduce foreign exchange volatility and transnational political risks. In this regard, Alrasheed and Al-Musaed (2022) advocate for improved risk modeling that takes into account the unstable nature of cross-border cooperation as well as the changing geopolitical environment.

Multi-criteria and Fuzzy Decision-Making Techniques

In Saudi Arabia, multi-criteria decision making (MCDM) techniques—specifically, goal programming and the Analytic Hierarchy Process (AHP)—have been used to assess trade-offs in major infrastructure projects. Guizani et al. (2023) demonstrated the significance of using financial stress indicators in risk models by using MCDM to quantify the effect of economic policy uncertainty on corporate cash holding habits among Saudi enterprises.

This procedure is further improved by fuzzy logic, which models ambiguous inputs and subjective assessments, which are prevalent in fields like environmental planning and public health. For example, Saudi public agencies employed fuzzy inference methods to forecast hospital capacity and forecast virus propagation in the face of uncertainty during the COVID-19 pandemic (Ministry of Health, 2021). These illustrations show how qualitative insight and quantitative rigor can be combined in dynamic risk contexts.

Emerging Role of AI and Predictive Analysis

These days, machine learning algorithms and artificial intelligence (AI) are crucial in changing risk analysis. Predictive models may produce probabilistic forecasts across domains and continuously learn from fresh data inputs. According to Swiss Quality Consulting (2024), AI-based risk platforms are being used more and more in Saudi Arabia’s public safety, logistics, and financial services sectors. By improving situational awareness, these tools facilitate risk avoidance and real-time decision-making.

Additionally, AI improves scenario planning through sentiment analysis and natural language processing, which are particularly helpful in tracking public opinion, policy changes, and sociopolitical developments—all of which are pertinent to Vision 2030 reforms.

Conceptual Gaps and Research Contributions

Despite these developments, a significant gap still exists: most research treats dynamic risk components (such as fuzzy logic, AI, or MCDM) separately without a cohesive framework that methodically integrates various methods. Furthermore, the breadth and depth of empirical applications specific to Saudi Arabia’s Vision 2030 goal are constrained.

In order to fill in these gaps, this paper:

• putting forward an integrative framework that blends AI analytics, fuzzy logic, MCDM, and real-time monitoring.
• Using case studies in public health and infrastructure that are unique to Saudi Arabia, the framework is tested.
• Coordinating the model with Vision 2030’s national agendas and governance frameworks.

Figure 1: Framework Component

Scope (1 = low, 5= high)

To explain, Saudi Arabia’s integrated framework for dynamic risk management. The approach shifts from linear, static frameworks to adaptive systems that use AI-driven analytics, fuzzy logic, and multi-criteria decision-making (MCDM). Within the framework of Vision 2030 megaprojects and regulatory evolution, this integrative strategy improves responsiveness, agility, and long-term policy alignment. (Source: Author’s own explanation, drawing from Alrasheed & Al-Musaed (2022), Ahmed et al. (2021), Paltrinieri et al. (2013), and Hillson (2009)).

Comparative evaluation of risk management elements pertinent to the modernization of Saudi Arabia. In terms of complexity, adaptability, and national significance, the suggested dynamic framework performs better than conventional models, facilitating strategic alignment with Vision 2030. Alrasheed & Al-Musaed (2022), Ahmed et al. (2021), Hillson (2009), and Sarbayev et al. (2019) were all incorporated into the author’s own elaboration.

PROPOSED FRAMEWORK: INTEGRATING DYNAMIC RISK MANAGEMENT IN SAUDI ARABIA

This study suggests a novel, multi-layered dynamic risk management paradigm that combines fuzzy logic, multi-criteria decision-making (MCDM), and artificial intelligence (AI)-driven analytics to handle the changing risk landscape brought about by Saudi Arabia’s Vision 2030. The complexity, unpredictability, and non-linearity of contemporary hazards in Saudi Arabia’s governmental and commercial sectors are addressed by this integrated approach.

Framework Overview

There are five interacting layers in the suggested framework:

1. Real-Time Data Gathering and Tracking.
2. Quantification of Risk using Fuzzy Logic Systems.
3. Optimizing Decisions Through MCDM Methods.
4. Predictive analytics and artificial intelligence for scenarios that look ahead.
5. Mechanisms for Governance and Policy Feedback.
6. Continuous feedback loops underpin each layer, enabling decision-makers to constantly reevaluate, reorder, and adjust risk measures.

Layer 1: Real-time Data Acquisition and Monitoring

The foundation of dynamic risk perception is real-time data collection. Institutions can gather ongoing data streams about financial indicators, environmental measures, public sentiment, and geopolitical changes by using IoT (Internet of Things) devices, blockchain records, satellite imaging, and government databases.

For example, sensors built into NEOM’s smart infrastructure may track energy efficiency, water consumption, and structural stress in real time. In a similar vein, during an outbreak, the Ministry of Health’s real-time dashboards can monitor hospital occupancy or vaccination stock.

Layer 2: Fuzzy Logic-based Risk Quantification

Fuzzy logic, as opposed to binary or probabilistic risk measures, captures subjectivity and ambiguity in expert opinions. Fuzzy risk scores, for instance, can stand in for verbal evaluations like “highly probable,” “moderately severe,” or “uncertain but critical.”

Fuzzy logic allows for a more sophisticated risk assessment model in areas like public policy planning and infrastructure construction, where data may be lacking or expert opinions may differ.

In the Saudi construction business, Ahmed et al. (2021) showed how well fuzzy modeling works for assessing time, financial, and regulatory risks.

Layer 3: Multi-criteria Decision Making (MCDM)

MCDM methodologies such as the Analytic Hierarchy Process (AHP), TOPSIS, and PROMETHEE offer strong frameworks for evaluating and prioritizing options because risks in Vision 2030 projects frequently entail trade-offs between cost, time, social effect, and sustainability.

Decision-makers benefit from this layer:

• Examine competing proposals for infrastructure.
• During emergencies, give priority to health interventions.
• Distribute resources among sectors in an unpredictable environment.

When the government, foreign investors, and civil society have conflicting agendas, MCDM facilitates transparency and stakeholder alignment in decision-making. Guizani et al. (2023) shown that in the Saudi corporate sector, MCDM models enhanced financial decision-making in the face of economic uncertainty.

Layer 4: AI and Predictive Analysis

By identifying nonlinear patterns and modeling intricate relationships between several variables, AI-driven models improve risk forecasting. What predictive analytics can do:

• Estimate the volatility of the oil price and the effects it has on the economy.
• Forecast project delays using labor, weather, and procurement data.
• Examine social media sentiment patterns to identify reputational hazards.

In addition to continuously increasing accuracy, machine learning techniques (such as Random Forest and LSTM neural networks) can assist in simulating “what-if” situations to assess the robustness of policies.

The significance of AI in regional risk dashboards for real-time public safety and fiscal resilience is emphasized. (Sarbayev et al., 2019)

Layer 5: Governance and Policy Feedback

Governance frameworks and policy loops are essential to risk management. This layer links stakeholder discussions, legal reviews, and national planning procedures to risk assessments. Additionally, it offers methods for:

• modifying regulatory structures in light of recognized risk patterns.
• adjusting national budgets to account for new economic threats.
• incorporating into routine operating procedures the lessons acquired from emergencies (like COVID-19).

Future desalination projects, for instance, can benefit from lessons learned from a failed water infrastructure project by revising the regulatory architecture and risk model.

According to Hammad (2024), dynamic risk frameworks might just remain theoretical exercises in the absence of institutional integration.

Benefits of the Integrated Approach

The suggested paradigm provides the following benefits by fusing institutional governance with technology tools (MCDM, fuzzy logic, AI):

• Flexibility: Adapts quickly to changing circumstances.
• Real-time, evidence-based interventions are made possible by responsiveness.
• Transparency: Encourages public decisions to be trusted by stakeholders.

Strategic Alignment: Promotes Vision 2030’s long-term goals, especially those related to infrastructural resilience, regulatory modernization, and economic diversification.

METHODOLOGY

Design

Using a mixed-methods approach, this study combines qualitative analysis and quantitative modeling. The technique is set up to use both empirical data and professional opinion from Kingdom of Saudi Arabia stakeholders to validate the effectiveness of the suggested dynamic risk management framework. Selected Vision 2030 projects—particularly infrastructure projects in the NEOM and Qiddiya growth zones—are subjected to a case study approach.

Data Sources

Information was gathered from several main and secondary sources:

• 22 experts from several ministries (Economy, Investment, and Energy), regulatory agencies (SAMA, Capital Market Authority), and project managers from NEOM and Red Sea Global participated in semi-structured interviews to gather primary data.
• Secondary data sources include peer-reviewed literature from 2018 to 2024, financial risk disclosures, government reports (Vision 2030 strategic documents), and ESG dashboards.

The following sources provided the quantitative data utilized in the modeling:

• GASTAT, the General Authority for Statistics.
• Central Bank of Saudi Arabia (SAMA).
• Databases from the World Bank and IMF (macroeconomic indicators).
• IoT device real-time feeds for two prototype infrastructure initiatives.

Model Implementation

The following models and techniques were used:

• Models of fuzzy logic are used to account for uncertainty in risk assessment for construction and regulatory delays. Risk likelihood and severity were represented by triangular fuzzy numbers that included expert subjective judgments.
• The five project variables of cost, time, environmental sustainability, reputational impact, and strategic alignment with Vision 2030 are all prioritized using MCDM (TOPSIS and AHP).
• Predictive AI Models: TensorFlow was used to create LSTM neural networks in Python that can forecast delays based on a time series of weather variability, regulatory clearances, and material delivery.
• Validation Tools: The internal consistency of expert judgment scores was established by Cronbach’s alpha (α = 0.87). Testing the fuzzy-MCDM model’s resilience to changes in weightings, was done via sensitivity analysis.

RESULTS AND DISCUSSION

Effectiveness of the framework

The suggested framework performed well across all criteria, according to the results of the pilot applications:

More accurate depictions of uncertainty were made possible by the fuzzy risk scoring method, particularly in relation to ESG and reputational issues, which are frequently disregarded in conventional linear models. With more than 80% accuracy, the AI-based prediction models offered early warnings for possible delays brought on by supply chain interruptions. Stakeholders responded favorably to the MCDM prioritizing outputs, which promoted consensus-building and offered a clear visual rating of intervention alternatives.

Implications for Saudi Arabia

The results show that using dynamic risk modeling can greatly enhance Saudi Arabia’s large-scale transitions’ ability to make decisions in the face of uncertainty. This is particularly crucial in light of:

• the interconnectedness of hazards in the housing, water, energy, and financial sectors.
• the requirement that public-private partnership (PPP) frameworks be transparent.
• the need for long-term national strategic plans to be resilient in the face of geopolitical and climate change.

Furthermore, the framework encourages:

• By incorporating feedback loops into ministerial decision cycles, agile governance is achieved.
• The increased predictability of risk-adjusted returns for international stakeholders makes investments more appealing.
• Adherence to global guidelines like ISO 31000 and ESG disclosure requirements.

Limitations

Despite its advantages, the framework has drawbacks.

• high reliance on the quality of real-time data.
• Subjectivity may be introduced via the need for expert calibration of fuzzy models.
• Dynamic modifications to risk protocols may encounter resistance from institutional inertia.

Future studies ought to concentrate on:

• using audit trails based on blockchain technology to enhance data integrity.
• evaluating the model’s applicability to other countries in the Gulf Cooperation Council (GCC).
• utilizing NLP to create automated regulatory update systems.

REFERENCES

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