Risk Scoring Model Documentation
Technical Specification of Risk Scoring Algorithms, Threat Category Classification, and Configurable Scoring Models
Executive Summary
Blockscout AI's Risk Scoring Model is an advanced, AI-driven system engineered to evaluate the risk profiles of blockchain addresses, entities, and transactions. Leveraging a robust multi-dimensional framework, the model integrates entity analysis, transaction behavior monitoring, and jurisdictional assessment to deliver comprehensive risk intelligence. This enables financial institutions, cryptocurrency exchanges, compliance teams, and threat intelligence operations to identify, mitigate, and respond to potential threats in real-time.
At its core, the model employs a flexible, weighted scoring algorithm that incorporates dozens of risk factors across key threat categories. These factors draw from on-chain behavioral patterns, entity attribution data, regulatory sanctions lists, and global compliance databases. The result is a transparent, auditable risk score that supports configurable models tailored to specific regulatory environments or organizational needs.
By subscribing to Blockscout AI's data feed services, organizations gain access to continuously updated risk intelligence, powered by our proprietary clustering algorithms and real-time on-chain monitoring. This not only enhances compliance workflows but also establishes a proactive defense against emerging threats, fostering trust with regulators and stakeholders while minimizing operational risks.
1. Risk Scoring Architecture
1.1 Multi-Pillar Framework
The Risk Scoring Model is built on a balanced, multi-pillar architecture, where each pillar addresses a distinct aspect of risk. This modular design allows for customization, ensuring adaptability to diverse compliance and threat intelligence scenarios.
Entity Risk Pillar
Focuses on the inherent characteristics of the entity controlling the address, including type, historical behavior, regulatory status, and sanctions exposure. This pillar establishes a baseline risk by evaluating who or what is behind the activity.
Transaction Risk Pillar
Examines patterns in transaction flows, including velocity, volume, and interactions with counterparties. It detects anomalies indicative of illicit activities, such as money laundering or obfuscation techniques.
Jurisdiction Risk Pillar
Assesses geographic and regulatory risks based on associated locations, incorporating global compliance data to highlight exposure to high-risk regions or sanctioned territories.
These pillars are combined through a weighted aggregation, with emphasis placed on the most critical dimensions based on the use case. Configurable weights allow users to prioritize pillars—for instance, emphasizing jurisdiction risk in cross-border compliance scenarios.
1.2 Overall Risk Calculation
The final risk score is derived from a weighted summation of the pillar scores, normalized to a 0-100% scale. Special override mechanisms ensure immediate escalation for critical threats, such as sanctions matches, setting the score to maximum risk regardless of other factors.
- Low Risk (0-33%): Indicates routine, low-threat activity with established, compliant entities.
- Medium Risk (34-66%): Suggests potential concerns requiring review, such as unusual patterns or moderate exposure.
- High Risk (67-100%): Signals significant threats, warranting enhanced due diligence, transaction blocking, or reporting.
This interpretable output, complete with color-coded indicators (green for low, yellow for medium, red for high), empowers users to make swift, informed decisions.
2. Entity Risk Calculation
Entity Risk evaluates the controlling party's profile, classifying threats based on type, history, and compliance status. This pillar is foundational, as it contextualizes all subsequent activity.
2.1 Entity Type and Threat Category Classification
Entities are classified into threat categories using a proprietary database informed by regulatory guidelines and historical data. Categories include:
Each category assigns a base risk level, adjustable via configurable models to align with specific threat intelligence needs (e.g., heightened scrutiny for certain illicit activities).
2.2 Entity Age and Stability Factors
Longer operational history generally correlates with lower risk. The model applies graduated risk adjustments based on age brackets, with newer entities flagged for closer monitoring due to limited track records. Unknown ages default to a moderate risk to encourage further investigation.
2.3 Tags, Modifiers, and Compliance Indicators
Additional risk signals come from entity tags, such as regulatory flags or behavioral markers. These modifiers are aggregated additively, with caps to prevent over-scoring. Key examples include penalties for lacking customer verification processes or associations with high-threat activities.
Sanctions screening, aligned with global lists like OFAC, triggers overrides for maximum risk, ensuring compliance primacy.
2.4 Beneficial Owner Analysis
For layered ownership structures, the model traces beneficial owners, overriding primary entity data if discrepancies exist. This configurable depth enhances threat detection in complex networks.
Aggregation Approach: Entity Risk blends base category scores with modifiers and stability factors, producing a pillar score that feeds into the overall model.
3. Transaction Risk Calculation
Transaction Risk scrutinizes behavioral and relational patterns, classifying threats through heuristic analysis and counterparty evaluation.
3.1 Counterparty Risk Assessment
This core component weighs interactions with other entities, distinguishing incoming and outgoing flows. Risks are aggregated using amount-weighted averages, with configurable thresholds for high-threat counterparties (e.g., sanctioned or illicit entities). Multi-hop analysis extends this to indirect connections, decaying influence per hop.
3.2 Behavioral Heuristics and Threat Detection
Multiple heuristics detect anomalies across dimensions like volume, frequency, timing, and obfuscation methods (e.g., privacy protocols). Threat categories here include:
Structuring Patterns
Burst activity or imbalanced flows suggesting evasion
Obfuscation Threats
Use of mixing techniques or rapid transfers
Volume Anomalies
Extreme high or low activity relative to norms
Configurable models allow tuning these heuristics for specific threats, such as focusing on exploitation-related patterns.
3.3 Address and Velocity Factors
Address longevity and transaction speed provide additional signals, with recent or bursty activity elevating risk.
Aggregation Approach: Transaction Risk combines these elements proportionally, with overrides for critical detections like sanctions-linked flows.
4. Jurisdiction Risk Calculation
Jurisdiction Risk maps geographic exposures to compliance databases, classifying countries by regulatory strength and threat levels.
4.1 Location Identification and Mapping
Countries are derived from entity data, with fallbacks for unknown or darknet-associated cases. Multi-country scenarios average risks, preserving individual breakdowns for transparency.
4.2 Risk Scoring and Threat Categories
Scores reflect factors like regulatory frameworks and historical issues, categorized as low, medium, or high-threat jurisdictions. Configurable overrides allow alignment with user-specific watchlists.
Aggregation Approach: Averages are computed with defaults for incomplete data, ensuring comprehensive coverage.
5. Advanced and Configurable Features
5.1 Model Configurability
Users can customize weights, thresholds, and threat categories via our API or dashboard, tailoring models for scenarios like AML compliance or targeted threat intelligence.
5.2 Multi-Hop and Network Analysis
Extends risk assessment beyond direct interactions, configurable for depth and decay.
5.3 Real-Time Processing and Caching
Optimized for scale with intelligent caching and batching, supporting high-volume feeds. Real-time updates integrate new data seamlessly.
5.4 Transparency and Auditability
Detailed factor breakdowns accompany every score, enabling regulatory reporting and trust-building.
6. Output Format and Integration
Risk outputs are delivered in structured JSON, including pillar scores, factors, color codes, and metadata. Integration with Blockscout AI's data feeds provides streaming access for continuous monitoring.
{
"overallRisk": 0.72,
"entityRisk": {
"aggregateScore": 0.85,
"factors": [...]
},
"transactionRisk": {
"aggregateScore": 0.68,
"factors": [...]
},
"jurisdictionRisk": {
"aggregateScore": 0.45,
"factors": [...]
},
"modelVersion": "2.1.0"
}
7. Performance, Scalability, and Compliance Alignment
Engineered for enterprise-scale, with optimizations like parallel processing and fault-tolerant fallbacks. Aligned with FATF, OFAC, and global standards, the model supports auditable trails and regulatory reporting.
Conclusion
Blockscout AI's Risk Scoring Model sets a new standard in blockchain threat intelligence, combining rigorous algorithms with configurable flexibility to deliver authoritative insights. By establishing transparency and precision, it builds trust while empowering organizations to navigate complex risks.
To harness this power for your compliance and intelligence needs, subscribe to our data feed services today. Contact us at sales@blockscout.ai for a demo and integration guide—unlock real-time, actionable intelligence that drives security and growth.