Insurance | Lucid Sphere AI

Insurance Data Analytics Software: Key Features

Batch and real-time aggregation of insurance data.
Support for multiple data formats: text, digital images, video, IoT device readings, etc.
Optical character recognition (OCR) for automated conversion of paper documents into a digital format.
ML-enabled capture and validation of data (e.g., customer information, policy terms, claim details) provided in digital insurance documents.
Automated identification of missing, mismatched, or inaccurate insurance data.
Auto-fixing (deduplicating, standardizing, etc.) erroneous records or routing them for a manual check.

Centralized storage for raw and processed insurance data.
Metadata storage and auto-population (e.g., to onboard customers faster, register insured assets, create and renew insurance policies).
Automated data backup and recovery.
Search engine with filtering and metadata querying to navigate insurance data and documents.

Sales analytics: insurance sales by period, location, insurance type; average revenue per agent, new policies per agent.
Customer analytics: new customers by period, retention and churn rate by customer segment, CLV.
Underwriting analytics: customer risk score, average policy coverage amount, underwriting speed.
Claim management analytics: claim volume by period, average settlement time, average cost per claim, claim frequency, claim severity.
Finance analytics: total premiums by period, revenue per policyholder, the return on policyholder surplus, loss ratio.
Workforce analytics: quote rate, bind rate, quotas vs. production, and more.

ML-based analysis of historical insurance data to spot:
- Dependencies between multi-dimensional events, e.g., between unemployment rates and claims related to property theft or between medical treatment outcomes and health insurance expenses.
- Major change drivers for particular insurance metrics.
- Patterns and anomalies in customer behavior and insurance KPIs.
Identifying areas for improvement, e.g., suboptimal policy pricing, inadequate insurance agent workload, excessive loss reserves, etc.
Monitoring digital employee activities and identifying non-compliant behaviors.
Intelligent root cause analysis to understand the reasons behind events such as sudden insurance sales spikes, decrease in underwriter productivity, or systematic claims leakage.

Scenario modeling and what-if analysis for various insurance process areas: risk management, pricing, claim reserving, etc.
Intelligent forecasting of particular insurance metrics or events (e.g., loss cases, policy renewals, liquidity leakage) based on the analysis of:
Historical data on policyholder behavior and the insured asset performance.
Real-time lifestyle and behavior data in pay-as-you-live insurance.
Weather and natural disaster forecasts.
Traffic conditions.
Insights on global and local disease outbreaks.
Geopolitical situation.
Expected changes in legal regulations.

Calculating optimal insurance prices.
AI-powered decision-making on insurance claim approval or rejection.
Automated task assignment based on employee availability and qualification.
Determining the best-fitting service suppliers (e.g., healthcare providers, repair service providers) to handle claim-associated damage and injuries.
Recommendations for policyholders to prevent claim events (e.g., to undergo medical checkups, perform asset maintenance, or change a fleet route to avoid a high-risk area).
Planning the optimal claim expense budget.

Business intelligence dashboards for sales agents, underwriting specialists, claim managers, financial analysts, etc.
Configurable insurance data visualization formats, including:
Interactive pivot tables for customer data representation.
Heat maps for risk communication.
Symbol maps to reflect historical and projected insurance metrics by location.
Customizable templates for insurance sales reports, loss run reports, financial statements, etc.
Reports compliant with the necessary legal standards: IFRS 17 and NAIC for the US, Solvency II for the EU, SAMA and IA for the KSA, etc.
Scheduled and ad hoc report generation and automated distribution (internally and to the relevant legal regulators).

Full audit trail of manipulations with insurance data.
Permission-based access control.
AI-powered detection of fraudulent insurance transactions.
Data encryption in transit and at rest.
Data processing and storage in compliance with KYC/AML and OFAC requirements, CCPA, GLBA, SOC1 and SOC2, NYDFS (for New York), SAMA regulations (for the KSA), GDPR (for the EU), HIPAA (for health insurance), and more.
(optional) Insurance data hashing, timestamping, and recording in an immutable blockchain ledger.