Geospatial Risk Computer Vision
Problem: Inaccurate manual assessment of property attributes leading to adverse selection in CAT-prone areas.
Solution: Sabalynx deploys Convolutional Neural Networks (CNNs) to analyze high-resolution satellite and drone imagery. We automatically extract roof condition, material type, tree overhang, and defensible space metrics.
Data Sources: Nearmap, Airbus, and NOAA historical climate data.
Integration: API-driven injection of “Risk Scores” into the policy administration system (PAS).
Outcome: 18% improvement in loss ratio; 90% reduction in manual inspection costs.
CNNComputer VisionCAT Modeling
RAG-Powered APS Summarization
Problem: Underwriters spend 4-6 hours reviewing 300-page Attending Physician Statements (APS), delaying policy issuance.
Solution: We implement a Retrieval-Augmented Generation (RAG) pipeline using specialized medical LLMs to ingest unstructured EHR data. The system identifies morbidity triggers, ICD-10 codes, and medication non-compliance instantly.
Data Sources: Unstructured PDF/TIFF medical records, lab results.
Integration: Secure FHIR-compliant data connectors.
Outcome: 75% reduction in review time; increased underwriting throughput by 4x without headcount growth.
LLMRAGNLPEHR
Continuous Exposure Monitoring
Problem: Cyber risk is dynamic, but underwriting remains annual and static, leading to catastrophic aggregation of risk.
Solution: Our platform utilizes active scanning agents to monitor a prospect’s external attack surface (CVEs, open ports, leaked credentials) in real-time. We use Gradient Boosted Trees to correlate technical vulnerabilities with breach probability.
Data Sources: Shodan, BitSight, Dark Web feeds.
Integration: Dynamic pricing engine recalculating GWP monthly based on security posture.
Outcome: 22% reduction in loss frequency; improved risk-adjusted pricing precision.
Predictive AnalyticsCyber-GraphAPI
Behavioral Safety Prediction
Problem: Workers’ Comp pricing relies on outdated NCCI class codes, ignoring actual safety culture and near-miss data.
Solution: We build deep learning models that ingest telematics and IoT data from the worksite to predict accident propensity. We identify high-risk behaviors (repetitive motion strain, ergonomic violations) before they become claims.
Data Sources: Wearable sensors, vision-based safety monitoring, OSHA filings.
Integration: Enterprise Data Warehouse (EDW) to PAS synchronization.
Outcome: 15% reduction in incident rate; competitive advantage in high-hazard niches.
Deep LearningIoTPredictive Safety
Digital Footprint Underwriting
Problem: Small business applicants often misrepresent their operations, leading to premium leakage and misclassification.
Solution: Sabalynx deploys NLP scrapers to verify business activities from websites, social media, and Google reviews. Our models reconcile self-reported data against the “Digital Truth” to automate STP for 80% of applications.
Data Sources: Web scrapers, Yelp, LinkedIn, Secretary of State records.
Integration: Real-time underwriting rules engine (Drools/Camunda).
Outcome: 40% increase in binding conversion; 12% recovery of premium leakage.
STPNLP ScraperIdentity Resolution
Cargo Sensor-Based Pricing
Problem: Cargo underwriting uses static route tables, ignoring actual transit conditions (temp, humidity, shock).
Solution: We implement an Edge-AI gateway that ingests IoT telemetry from shipping containers. Bayesian inference models adjust risk weightings dynamically based on climate volatility and transit delays.
Data Sources: AIS vessel tracking, IoT sensor arrays, weather APIs.
Integration: Blockchain-based smart contracts for automated premium adjustment.
Outcome: 30% reduction in spoilage claims; superior pricing for cold-chain logistics.
Bayesian InferenceEdge AITelematics
Treaty Clause Analytics (Legal LLM)
Problem: Reinsurers face massive basis risk when treaty wording differs across ceding companies.
Solution: We deploy a specialized Transformer model fine-tuned on insurance law to identify “silent” risks and inconsistent exclusions across thousands of treaty documents. Semantic search enables instant exposure mapping across the book.
Data Sources: Historical treaty archives, legal precedents.
Integration: Enterprise Search (Elasticsearch) + LLM UI.
Outcome: Eliminated $50M+ in unintended coverage exposures; 90% faster renewal audits.
TransformerLegal AIRisk Mapping
Telematics Behavioral Scoring
Problem: Fleet underwriting relies on driver age and location, ignoring the predictive power of second-by-second driving data.
Solution: Sabalynx builds Recurrent Neural Networks (RNNs) to process high-frequency GPS and accelerometer streams. We define proprietary “Safe Driver” signatures that outperform traditional actuarial models by 40%.
Data Sources: OBD-II devices, mobile telematics, traffic density APIs.
Integration: Real-time quoting engine for fleet managers.
Outcome: 25% decrease in claim frequency; high retention of profitable “low-risk” fleets.
RNNTime-SeriesFleet AI