| Key Takeaways |
| Scenario planning and stress testing serve fundamentally different purposes: scenario planning explores strategic what-if outcomes across business drivers, while stress testing quantifies capital adequacy and loss absorption under extreme but plausible conditions. Most organizations need both capabilities. |
| Anaplan dominates enterprise scenario planning with its Hyperblock engine enabling real-time recalculation across unlimited what-if models, but lacks native Monte Carlo simulation and regulatory stress testing capabilities required for CCAR/DFAST compliance. |
| Palantir Foundry operates as the enterprise data integration layer, connecting disparate data sources to enable AI-powered scenario modeling. Its strength is creating digital twins of entire operations for predictive analytics, not pre-built financial planning templates. |
| Moody’s Analytics leads regulatory stress testing with Chartis RiskTech100 #1 ranking for 2026, offering end-to-end CCAR/DFAST compliance including scenario generation, credit loss modeling, and automated FR Y-14 regulatory reporting for banks with $250B+ in assets. |
| SAS delivers the deepest statistical modeling engine for stress testing, combining decades of analytics expertise with ML-driven anomaly detection. Best for organizations with strong data science teams seeking maximum customization of risk models. |
| Oracle Cloud EPM provides the tightest integration for organizations already invested in the Oracle ecosystem, covering planning, budgeting, forecasting, consolidation, and scenario modeling within a single cloud platform. |
| Risk managers should map platform selection to their specific use case: strategic FP&A planning (Anaplan/Oracle), regulatory capital stress testing (Moody’s/SAS), or data-intensive operational scenario modeling (Palantir). |
The Federal Reserve’s 2025 CCAR severely adverse scenario modeled an 8.5% real GDP decline, unemployment peaking at 10%, equity markets falling 55%, and commercial real estate prices dropping 40% over a 9-quarter horizon.
Banks with consolidated assets over $250 billion must demonstrate capital adequacy through these scenarios or face restrictions on dividend payments and share repurchases.
Beyond regulatory mandates, the BCG Strategic Foresight 2025 report found that organizations combining AI-driven signal detection with rigorous scenario analysis outperform peers in resilience and strategic agility.
Scenario planning and stress testing are distinct but complementary risk management disciplines. Scenario planning explores how different strategic assumptions play out across business drivers, typically deterministic what-if comparisons.
Stress testing subjects the organization to extreme but plausible conditions to quantify losses and capital needs, often using probabilistic methods like Monte Carlo simulation. Under ISO 31000, both techniques fall under Clause 6.4.3 (Risk Analysis), and COSO ERM Principle 12 requires organizations to identify risks that could affect strategy execution through structured scenario analysis.
This guide compares five platforms spanning both disciplines: Anaplan, Palantir Foundry, Moody’s Analytics, SAS, and Oracle Cloud EPM.
Each is evaluated through the lens of enterprise risk assessment methodology, mapping capabilities to the use cases ERM practitioners encounter daily: strategic planning, regulatory compliance, operational resilience testing, and board-level risk reporting.
Scenario Analysis vs Stress Testing: Defining the Landscape
Risk practitioners frequently conflate scenario analysis and stress testing, but they operate at different points in the risk management lifecycle.
Understanding the distinction is critical for platform selection because no single tool excels at both. The table below maps the key differences to help practitioners select the right capability for their specific needs.
Scenario Analysis vs Stress Testing: Key Differences
| Dimension | Scenario Analysis / Planning | Stress Testing |
| Purpose | Explore strategic outcomes under different business assumptions; support decision-making on investment, pricing, growth, and resource allocation | Quantify losses, capital adequacy, and survival capacity under extreme but plausible adverse conditions |
| Methodology | Deterministic what-if models, sensitivity analysis, driver-based forecasting; increasingly AI-augmented | Probabilistic modeling (Monte Carlo, historical simulation), reverse stress testing, regulatory prescribed scenarios |
| Typical Output | Multiple scenario comparisons (best/base/worst), strategic recommendations, variance analysis | Capital impact quantification, loss distributions, liquidity projections, regulatory reports (FR Y-14) |
| Regulatory Mandate | Generally voluntary; encouraged under COSO ERM Principle 12 and ISO 31000 Clause 6.4.3 | Mandatory for banks $250B+ (CCAR), $10B+ (DFAST); required under Basel III, Solvency II, ORSA |
| Time Horizon | 1-5+ years; strategic planning cycles aligned with board strategy | 9-quarter regulatory horizon (CCAR); 1-3 years for internal stress programs |
| Users | FP&A, corporate strategy, CRO office, board risk committee | Capital planning, credit risk, market risk, ALM, regulatory reporting teams |
| ERM Framework Fit | COSO ERM Principles 6-9 (Strategy & Risk); ISO 31000 Clause 5.4.2 (Context) | COSO ERM Principles 10-12 (Performance); ISO 31000 Clause 6.4.3 (Analysis) |
Evaluation Framework for Scenario and Stress Testing Platforms
Platform selection depends on which use cases dominate your organization’s risk assessment process.
A bank’s CCAR compliance team needs fundamentally different capabilities than a manufacturing company running supply chain disruption scenarios. The framework below organizes evaluation criteria by use case.
Six-Domain Evaluation Criteria
| Domain | What to Assess | Why It Matters for ERM | Key Questions |
| 1. Modeling Engine | Deterministic vs probabilistic; in-memory calculation; Monte Carlo support | Deterministic-only tools miss tail risks that probabilistic models capture | Does the platform support Monte Carlo simulation natively or require add-ons? |
| 2. Regulatory Compliance | CCAR/DFAST scenario support, FR Y-14 reporting, Basel III/IV alignment | Non-compliant stress testing outputs trigger MRAs and capital distribution restrictions | Can the tool auto-generate regulatory reports in the required format? |
| 3. Data Integration | Multi-source data ingestion, ERP connectivity, real-time feeds, data quality | Scenarios built on incomplete data produce misleading results | Can the platform ingest data from your core banking, ERP, and market data feeds? |
| 4. Scalability & Speed | Model size limits, recalculation speed, concurrent users, cloud elasticity | Large banks run thousands of scenarios across millions of positions; speed matters | What is the recalculation time for a full scenario across your portfolio? |
| 5. Collaboration & Governance | Workflow, version control, audit trails, role-based access, model validation | Regulators expect documented model governance with change control and audit history | Can you trace every model change, assumption, and approval through an audit trail? |
| 6. Visualization & Reporting | Board-ready dashboards, sensitivity charts, tornado diagrams, variance analysis | Board risk committees need intuitive scenario comparison visuals, not raw data | Can the tool generate board-ready reports with scenario comparison visualizations? |
Head-to-Head: Five Platforms Compared
The following comparison evaluates Anaplan, Palantir Foundry, Moody’s Analytics, SAS, and Oracle Cloud EPM across the six evaluation domains.
Each platform addresses different points on the scenario-to-stress-testing spectrum.
Platform Comparison Matrix
| Capability | Anaplan | Palantir Foundry | Moody’s Analytics | SAS / Oracle EPM |
| Core Strength | Connected enterprise scenario planning with unlimited what-if models | AI-powered data integration and digital twin creation for operational scenarios | End-to-end regulatory stress testing (CCAR/DFAST) with credit and market risk models | SAS: Deep statistical modeling; Oracle: Integrated EPM within Oracle ecosystem |
| Modeling Engine | Hyperblock in-memory engine; deterministic with AI-augmented forecasting (PlanIQ) | Ontology-based AI platform; custom models via Python/SQL; agentic AI for automation | Proprietary econometric models; Monte Carlo; Kamakura default models; scenario generation | SAS: Advanced analytics engine with ML; Oracle: Driver-based with Narrative Reporting |
| Scenario Types | Unlimited what-if; sensitivity; driver-based; side-by-side comparison | Custom scenario pipelines; digital twin simulation; graph analytics for network effects | Baseline, adverse, severely adverse (CCAR); custom internal; reverse stress testing | SAS: Custom statistical scenarios with Monte Carlo; Oracle: Planning scenarios with consolidation |
| Regulatory Support | Limited; no native CCAR/DFAST reporting | Configurable but not pre-built for banking regulation | Full CCAR/DFAST: FR Y-14/Y-16 auto-generation; Basel III/IV; Solvency II; ORSA | SAS: Strong regulatory analytics; Oracle: IFRS/GAAP consolidation but limited CCAR |
| Data Integration | 200+ pre-built connectors; Anaplan Data Hub; real-time ERP feeds | Unmatched: ingests from 1000+ systems; creates unified data ontology without modifying sources | Core banking feeds, market data, credit bureau; Moody’s proprietary economic data | SAS: Broad ETL; Oracle: Native Oracle DB/ERP integration; Fusion Cloud ecosystem |
| AI/ML Capability | PlanIQ (time-series forecasting), CoPlanner (conversational AI), Optimizer | AIP with LLMs; agentic AI for autonomous decision execution; graph neural networks | ML-enhanced credit scoring; AI-driven early warning indicators; scenario calibration | SAS: Industry-leading statistical ML; Oracle: Predictive planning with AI narratives |
| Deployment | Cloud SaaS; 4-6 month typical implementation; requires specialized consultants | Cloud/on-premises/hybrid; 4-12 week bootcamp-driven implementation with Palantir engineers | Cloud and on-premises; 6-12 month implementation for full CCAR suite | SAS: Cloud/on-premises; 6-9 months; Oracle: Cloud EPM; 4-8 months within Oracle ecosystem |
| Pricing | Enterprise subscription; typically $500K-$2M+ annually for mid-to-large enterprise | Enterprise custom; Forward Deployed Engineers included; typically $1M+ annually | Enterprise custom; module-based; $500K-$3M+ for full stress testing suite | SAS: Enterprise custom $400K-$1.5M; Oracle: Per-user cloud subscription with volume tiers |
| Best For | FP&A teams needing cross-functional strategic scenario planning at enterprise scale | Data-intensive organizations creating operational digital twins across complex systems | Banks and insurers with regulatory stress testing mandates (CCAR, DFAST, Solvency II) | SAS: Risk teams with data science depth; Oracle: Finance teams in Oracle ecosystem |
Individual Platform Profiles
Anaplan: Enterprise Connected Planning at Scale
Anaplan’s Hyperblock in-memory engine delivers instantaneous recalculation across unlimited scenario dimensions, connecting finance, operations, supply chain, HR, and sales planning into a unified model.
The platform excels at strategic what-if analysis: modeling how changes in pricing, headcount, market share, or supply chain disruption cascade across the entire organization in real time.
Anaplan Intelligence adds AI through three modules: PlanIQ for time-series forecasting based on historical drivers, CoPlanner for conversational AI model building, and Optimizer for constraint-based trade-off analysis.
Anaplan’s strength is breadth and connectedness. A single model can link operational risk management scenarios with financial impact analysis across every business unit simultaneously.
The platform supports over 200 pre-built integrations. Limitations include deterministic-only modeling (no native Monte Carlo), no built-in CCAR/DFAST regulatory reporting, implementation timelines of 4-6 months requiring specialized consultants, and a steep learning curve that restricts advanced modeling to power users.
Organizations needing probabilistic stress testing must supplement Anaplan with dedicated simulation tools.
Palantir Foundry: Data Integration and Digital Twin Modeling
Palantir Foundry operates as the data integration and decision intelligence layer, connecting data from 1,000+ disparate systems into a unified ontology without modifying source systems.
The platform creates digital twins of entire operations, enabling scenario modeling that incorporates real-world complexity: supply chain networks, customer behavior patterns, production dependencies, and financial outcomes all connected in a single analytical environment.
The 2023-launched AIP (Artificial Intelligence Platform) brings large language models into enterprise operations, enabling natural language interaction with scenario models.
Palantir’s Forward Deployed Engineers work directly with customer teams through bootcamp-style implementations, building AI-powered scenario applications using the client’s actual data in 4-12 weeks.
The platform supports custom model development in Python and SQL, graph analytics for network risk scenarios, and agentic AI that can autonomously execute decisions.
Limitations include no pre-built financial planning templates (everything is custom-built), opaque pricing models, a platform that requires technical expertise to maintain, and limited applicability to standard FP&A use cases where Anaplan or Oracle provide more structured experiences.
Palantir excels for organizations where data integration across complex systems is the primary risk assessment challenge.
Moody’s Analytics: Regulatory Stress Testing Authority
Moody’s Analytics holds the #1 position in the Chartis RiskTech100 for 2026, providing the most comprehensive regulatory stress testing suite for banks and insurers.
The platform covers the full CCAR/DFAST lifecycle: scenario generation using proprietary econometric models, credit loss estimation through Kamakura default models that predate regulatory scenarios by over a decade, market risk modeling, and automated FR Y-14, FR Y-16, and DFAST regulatory report generation.
The scenario engine produces baseline, adverse, and severely adverse scenarios calibrated to 28 economic variables over the required 9-quarter horizon.
Moody’s proprietary economic data and credit models provide a depth of financial risk intelligence that general-purpose planning tools cannot match.
AI and ML are embedded across solutions for early warning indicators, dynamic exposure management, and optimization of risk-adjusted returns. The platform supports risk quantification for board reporting with pre-built dashboards translating stress test outputs into board-ready visualizations.
Limitations include 6-12 month implementation timelines for full suite deployment, enterprise pricing that excludes smaller institutions, and a focus on financial services that limits applicability to non-regulated industries.
Moody’s is the definitive choice for any institution subject to CCAR, DFAST, Basel III/IV, or Solvency II mandates.
SAS Risk Management: Statistical Depth and Customization
SAS leverages decades of analytics leadership to deliver the most customizable stress testing platform on the market. The SAS Risk Management suite combines advanced statistical modeling, ML-driven anomaly detection, Monte Carlo simulation, and scenario analysis within a unified analytics engine.
Compliance teams can build bespoke risk models calibrated to their institution’s specific portfolio composition, geographic exposure, and asset class mix, going far beyond the one-size-fits-all approach that regulatory standard scenarios provide.
SAS excels for organizations with strong data science teams that want maximum control over model specification, calibration, and validation. The platform’s visual analytics enable tornado chart sensitivity analysis and custom risk model visualization.
SAS supports CCAR/DFAST scenarios with configurable regulatory analytics, though auto-generated regulatory reporting requires more configuration than Moody’s turnkey approach.
Limitations include complex implementation requiring significant IT resources, enterprise pricing that may not suit mid-size fintechs, and a steeper learning curve than cloud-native alternatives. SAS is best for institutions where customized risk modeling depth matters more than out-of-box regulatory templates.
Oracle Cloud EPM: Integrated Planning Within the Oracle Ecosystem
Oracle Cloud Enterprise Performance Management covers planning, budgeting, forecasting, consolidation, and scenario modeling in a single cloud platform.
The Planning module enables driver-based scenario analysis across revenue, cost, workforce, and capital spending with Narrative Reporting that automatically generates commentary on scenario variances. The real value comes from tight integration with Oracle ERP, Oracle Fusion Cloud applications, and the Oracle database, reducing data reconciliation and enabling automated data flows that eliminate manual extraction.
Oracle EPM supports what-if scenario modeling with side-by-side comparison, rolling forecasts, and three-point estimation for range-based planning.
The platform includes AI-driven predictive planning that augments traditional driver-based models with statistical forecasting.
Limitations include dependency on the Oracle ecosystem for maximum value, limited advanced stress testing capabilities compared to Moody’s or SAS, no native Monte Carlo simulation, and a platform better suited for finance-led planning than risk-team-led stress testing.
Oracle EPM is the strongest choice for finance teams already operating within the Oracle technology stack who need scenario planning capabilities without deploying a standalone risk platform.
Key Risk Indicators for Scenario and Stress Testing Programs
Running scenarios is the control. Measuring whether those scenarios actually improve risk-adjusted decision-making requires key risk indicators that connect scenario program outputs to enterprise risk outcomes.
Scenario and Stress Testing KRI Dashboard
| KRI | Target (Green) | Warning (Amber) | Breach (Red) | Data Source |
| Scenario runs completed per quarter | > 12 (monthly + ad hoc) | 6-12 | < 6 | Scenario platform usage log |
| Stress test capital adequacy buffer (CET1 ratio under stress) | > 200 bps above minimum | 100-200 bps | < 100 bps above minimum | CCAR/DFAST output reports |
| Model validation completion rate | 100% on schedule | 1-2 models delayed | > 2 delayed | Model risk management tracking |
| Scenario results presented to board per year | > 4 (quarterly minimum) | 2-4 | < 2 | Board risk committee minutes |
| Time from scenario request to results delivery | < 5 business days | 5-15 days | > 15 days | Scenario request tracking log |
| Percentage of strategic decisions with scenario support | > 80% | 50-80% | < 50% | Decision log cross-referenced with scenario library |
| Stress test MRA/MRIA findings (regulatory) | Zero open MRAs | 1-2 open MRAs | > 2 or any MRIA | Regulatory examination findings register |
| Reverse stress test completion (annual) | Completed with board review | Completed, pending review | Not completed | Annual reverse stress test program records |
These KRIs feed into your KRI dashboard alongside existing leading vs lagging indicators. The stress test capital buffer and MRA findings are the KRIs regulators scrutinize most aggressively.
Scenario run frequency and board presentation rate measure whether the program drives actual decisions or remains a compliance exercise.
Vendor Selection Decision Framework
Platform choice depends on your primary use case, regulatory obligations, existing technology stack, and team capabilities. The matrix below matches organizational profiles to recommended platforms.
Organizational Profile Matching
| Organization Profile | Primary Recommendation | Alternative | Key Decision Factor |
| Bank $250B+, CCAR/DFAST mandate | Moody’s Analytics | SAS | Turnkey regulatory stress testing with automated FR Y-14 reporting and proprietary economic scenarios |
| Bank $10-250B, DFAST mandate | SAS | Moody’s Analytics | Customizable stress testing with strong analytics depth at mid-tier pricing |
| Large enterprise, cross-functional FP&A | Anaplan | Oracle EPM | Connected planning across finance, operations, supply chain with unlimited scenario modeling |
| Oracle ecosystem, finance-led planning | Oracle Cloud EPM | Anaplan | Tightest integration with Oracle ERP and Fusion Cloud; lowest data reconciliation burden |
| Data-intensive operations, complex systems | Palantir Foundry | SAS | Unmatched data integration creating digital twins across 1,000+ source systems |
| Insurance company (Solvency II, ORSA) | Moody’s Analytics | SAS | Pre-built insurance risk models with Solvency II and ORSA regulatory support |
| Manufacturing / supply chain resilience | Anaplan | Palantir Foundry | Supply chain scenario planning with connected operational and financial modeling |
The First Quarter: From Selection to Strategic Value
| Phase | Actions | Deliverables | Success Metrics |
| Weeks 1-4: Assess and Align | Map current scenario/stress testing gaps against regulatory and strategic requirements; Define primary use cases (FP&A planning vs regulatory stress testing vs operational resilience); Evaluate 2-3 vendors against six-domain framework; Secure CRO and board sponsor | Gap analysis report mapping current vs required capabilities; Use case prioritization matrix; Vendor evaluation scorecard; Approved budget and implementation charter | Gaps documented and prioritized; Primary use case agreed; Vendor shortlisted; Executive sponsor confirmed |
| Weeks 5-8: Deploy and Model | Deploy platform with core data source integration; Build baseline scenario models (at minimum: base, upside, downside); Configure regulatory scenarios if applicable (CCAR baseline, adverse, severely adverse); Validate model outputs against historical data | Integrated platform with core data feeds; Three baseline scenario models operational; Regulatory scenario templates configured; Back-testing validation report | Platform connected to primary data sources; First scenario recalculates in < 60 seconds; Regulatory templates match FR Y-14 format; Back-test within 10% of actuals |
| Weeks 9-12: Operationalize and Govern | Establish model governance framework with change control and audit trails; Train risk, finance, and executive teams on scenario interpretation; Run first board-ready scenario presentation; Define ongoing cadence and escalation procedures | Model governance policy document; Training completion records; First board scenario report; Quarterly scenario calendar and RACI | Governance framework approved; 90%+ team trained; Board presentation accepted; Quarterly cadence locked in with CRO |
Where Scenario Programs Break Down
| Failure Mode | What Goes Wrong | How to Build It Right |
| Scenarios that confirm existing strategy rather than challenge it | Management designs scenarios to validate decisions already made, missing disruptive risks | Include independent scenario design by risk function; run reverse stress tests that start from failure |
| Models built on stale or incomplete data | Scenario outputs are only as good as the data feeding them; garbage in, garbage out | Automate data feeds from ERP/core banking; validate data quality before every scenario run |
| One-size-fits-all regulatory scenarios only | Running only CCAR-prescribed scenarios misses institution-specific vulnerabilities | Supplement regulatory scenarios with custom internal scenarios reflecting your actual risk profile |
| Scenario results never reach decision-makers | Risk team runs scenarios that sit in reports nobody reads or acts on | Require board-ready scenario summaries for every strategic decision; embed in capital planning cycle |
| Platform purchased but model governance absent | No change control, version history, or validation process for scenario models | Establish model risk management framework before deployment; align with OCC SR 11-7 guidance |
| Over-reliance on deterministic what-if without probabilistic analysis | Single-point scenarios create false precision; actual outcomes fall between scenarios | Supplement what-if models with Monte Carlo simulation to quantify probability distributions |
| Stress testing treated as annual compliance exercise | Running scenarios once a year for regulators misses the continuous risk monitoring benefit | Build always-on scenario capability; run ad hoc stress tests when material risks emerge between cycles |
Looking Ahead: Scenario and Stress Testing Trends for 2025-2027
The Federal Reserve’s proposed 2026 stress test scenarios introduce enhanced transparency through additional guides that narrow the ranges of potential variable values, making scenario design more predictable.
The Fed’s emphasis on reverse stress testing, where institutions identify which scenarios would cause failure rather than testing prescribed scenarios, is pushing banks to develop more sophisticated internal modeling capabilities.
ERM technology platforms will need to support reverse scenario generation as a standard feature rather than an advanced add-on.
AI-augmented scenario generation is transforming how organizations identify plausible futures. Anaplan’s PlanIQ, Palantir’s AIP, and Moody’s ML-enhanced credit models all represent the first wave of AI-native scenario capabilities.
By 2027, expect scenario platforms to automatically generate plausible stress scenarios based on real-time market signals, geopolitical indicators, and internal operational data without requiring manual scenario specification.
The 53% of finance teams now using hybrid scenario approaches (per Farseer’s State of Finance 2026 survey) will grow as tools make probabilistic modeling accessible to non-technical users.
Climate stress testing is becoming mandatory. The ECB already requires climate scenario analysis for EU banks, and US regulators are expected to formalize climate risk scenarios within CCAR by 2027.
Organizations should evaluate whether their scenario platform can model transition risks (carbon pricing, regulatory shifts) and physical risks (extreme weather, supply chain disruption) alongside traditional macroeconomic scenarios.
This convergence of climate risk with financial stress testing creates demand for platforms that bridge ESG sustainability KRIs and traditional financial risk models.
Operational resilience testing is expanding beyond financial services. Manufacturing, energy, healthcare, and government organizations increasingly run scenario exercises to test business continuity plans against cyber attacks, pandemic resurgence, geopolitical disruption, and AI-driven supply chain failures.
This trend broadens the addressable market for scenario platforms beyond their traditional banking and insurance base, making cross-functional tools like Anaplan and Palantir increasingly relevant for non-financial risk scenarios.
Ready to strengthen your scenario and stress testing capabilities? Visit riskpublishing.com for ERM frameworks, risk management consulting services, or contact us to discuss your organization’s scenario analysis and stress testing needs.
References
1. Federal Reserve: 2025 Stress Test Scenarios
2. Federal Reserve: Proposed 2026 Stress Test Scenarios
3. Chartis RiskTech100 2026: Moody’s Analytics #1 Overall
4. GARP: How to Make Stress Tests More Constructive and Informative
5. Moody’s Analytics: Stress Testing Solution Process Flow
6. Anaplan Connected Planning Platform
7. Palantir Foundry Enterprise Platform
8. SAS Risk Management Solutions
9. Oracle Cloud Enterprise Performance Management
10. OCC: Dodd-Frank Act Stress Test Requirements
11. ISO 31000:2018 Risk Management Guidelines
12. COSO ERM: Integrating with Strategy and Performance
13. Gartner Peer Insights: Anaplan vs Oracle Financial Planning 2026
14. BCG Strategic Foresight Report 2025
15. GARP: Stress Testing and Scenario Analysis Customization Challenge
Related Resources from riskpublishing.com
1. Scenario Analysis vs Stress Testing: When to Use Each
2. Monte Carlo Simulation in Risk Management
3. Tornado Chart Sensitivity Analysis
4. Three-Point Estimation (PERT)
5. Risk Quantification for Board Reporting
6. Enterprise Risk Management Frameworks
7. COSO vs ISO 31000 Comparison
9. How to Conduct a Risk Assessment
10. Business Continuity Plan Template
11. Key Risk Indicators for ESG and Sustainability
13. KRI Dashboard Best Practices
14. Operational Risk Management
Chris Ekai is a Risk Management expert with over 10 years of experience in the field. He has a Master’s(MSc) degree in Risk Management from University of Portsmouth and is a CPA and Finance professional. He currently works as a Content Manager at Risk Publishing, writing about Enterprise Risk Management, Business Continuity Management and Project Management.