Risk Management

New to this diagram? Learn about the method: Use Case Tree · Use Case · Outcome

Risk management integrates multiple risk factors including sanctions, PEPs, crime records, transaction patterns, and relationships to assess customer risk levels and determine appropriate due diligence measures. This use case addresses aggregating risk data from fragmented sources, maintaining dynamic risk profiles that evolve over time, applying consistent risk methodologies, understanding network risk propagation, enabling risk-based workflows, and providing explainable risk decisions with complete documentation for regulatory compliance.

The Challenge

Financial institutions struggle with comprehensive risk management:

• Multi-factor complexity — Risk assessment requires integrating numerous data points from disparate sources
• Dynamic risk profiles — Customer risk changes over time based on behavior, relationships, and external events
• Inconsistent methodologies — Different business units may apply risk assessment inconsistently
• Data fragmentation — Risk-relevant data scattered across systems, making holistic assessment difficult
• Regulatory requirements — Must demonstrate risk-based approach with appropriate documentation
• False positive management — Overly conservative risk models create unnecessary burden on low-risk customers
• Risk aggregation — Understanding cumulative risk across customer relationships and networks
• Model governance — Maintaining and updating risk models as regulations and threats evolve

Traditional risk management systems operate in silos and lack the integrated view needed for comprehensive risk assessment.

Why EKG is Required

Enterprise Knowledge Graphs enable sophisticated risk management:

• Holistic risk view — Integrate all risk factors (sanctions, PEPs, crime records, transactions, relationships) in one place
• Dynamic risk scoring — Continuously update risk ratings as new information becomes available
• Network risk analysis — Assess risk not just for individual customers but for entire networks and relationships
• Temporal risk tracking — Understand how customer risk evolves over time and identify risk trends
• Contextual assessment — Weight risk factors based on relationships, patterns, and context
• Risk-based workflows — Automatically route customers to appropriate due diligence processes based on risk
• Explainable risk decisions — Provide clear explanations of risk ratings and contributing factors
• Model integration — Connect risk models to customer data for real-time risk assessment

Business Value

• Enhanced risk detection — Identify high-risk customers more accurately through comprehensive assessment
• Regulatory compliance — Demonstrate risk-based approach with complete documentation and audit trail
• Resource optimization — Allocate due diligence resources based on actual risk levels
• Customer experience — Streamline onboarding for low-risk customers while maintaining security
• Proactive risk management — Detect emerging risks before they become compliance violations
• Cost reduction — Reduce false positives and optimize risk management operations