Custom Insurance Risk Management Software

• Batch or real-time aggregation of customer-associated risk data from internal and third-party sources:
  • Data submitted by customers for underwriting (bank statements, proof of employment, medical history, etc.).
  • The insured asset characteristics (value, location, conditions, etc.).
  • Location-specific climate, geopolitical, demographic, and economic data.
  • A customer’s past insurance coverage and claims history.
  • FNOLs and claim-supporting documents (medical reports, accident reports, etc.).

• Support for data in various formats: JSON, CSV, XML, PDF, text, digital images, recorded voice messages, streaming video, etc.
• AI-powered data pre-processing (structuring, cleansing, enrichment) and validation (including cross-referencing with third-party sources).
• Centralized storage of the collected data.

• Scenario modeling and analysis (including ML-based analysis, what-if analysis, event tree analysis, fault tree analysis, failure modes and effects analysis, etc.) to determine:
  • Insurance risk probability under particular customer-side events.
  • The impact of different insurance risk factors on the associated loss events, e.g., how the insured property characteristics affect P&C loss probability or how the client’s health state affects mortality.
• Monte Carlo simulations to predict and measure the impact of external uncertainty factors, such as accidents involving insured persons, natural disasters, geopolitical force majeure, changes in regulatory compliance requirements, etc.
• Scoring the potential insurance risk impact based on custom insurance actuarial formulas.
• Custom rules for individual and corporate customer segmentation by insurance risk classes.

Rule-based and ML-supported calculation of the expected loss amounts based on:

• Existing liabilities.
• Historical claim frequency and severity.
• Past claim payouts and expenses related to claim investigation and settlement.

• Calculating personalized and segment-specific insurance premiums based on the requested policy terms, customer risks and profitability, loss probability, region-specific taxes, and more.
• Calculating loss reserves under various reserving methods: distribution-free chain-ladder method, Bornhuetter–Ferguson method, average cost per claim method, etc.
• AI-based suggestions on the optimal loss reserves based on the analysis of historical and expected losses, premium and investment income, costs of unforeseen events, reinsurance coverage, and more.
• Real-time monitoring of the current vs. planned loss reserve budget utilization.

• AI- and IoT-enabled assessment of loss event probability based on:
  • Policyholder location, behavior, and health state.
  • The insured asset and asset environment state.
  • Weather and traffic conditions.
  • Healthcare, geopolitical, and economic sentiment.
• Real-time detection of high-risk events.
• Intelligent suggestions on the proper course of action for policyholders to prevent losses.
• Instant communication of risk mitigation steps to customers via an insurance app, email, or messaging apps.

• Automated calculation of quantitative key risk indicators (KRIs).
• Rule-based and AI-powered evaluation of qualitative KRIs.
• Configuring multi-level (low risk, moderate risk, high risk) insurance KRI thresholds based on:
  • AI-powered exposure analysis and suggestions on the appropriate risk limits.
  • User-defined rules (e.g., keeping potential risk impacts within certain tolerances).
• KRI analysis and real-time spotting of high-risk events.
• Automated risk segmentation and prioritization for handling based on the impact severity (expected financial losses, operational delays, etc.).
• Intelligent root cause analysis to determine the reasons behind the changes in particular insurance KRIs.
• Predictive analytics for risk trend identification.

• Collaborative creation of insurance risk mitigation strategies and plans.
• AI-based suggestions on the appropriate risk prevention measures, e.g., optimal insurance product pricing, loss reserving, cash reallocation between the insurer’s bank accounts to meet liquidity needs, task reassignment to handle urgent issues.
• Automated enforcement of risk mitigation steps (can involve insurance smart contacts to get full traceability of risk responses).
• Configurable timelines for the implementation of risk mitigation steps depending on the risk priority.
• Real-time monitoring of the risk mitigation progress and effectiveness.
• Instant messaging for the insurance teams to collaborate on risk management activities.

• Assigning particular risk control tasks to the best-fitting insurance specialists based on their role, qualification, schedule, etc.
• Configurable multi-department approval workflows for core insurance risks, KRIs, risk limits and exceptions.
• Permission-based access to particular pieces of risk information.
• End-to-end audit trail of user activities.
• Real-time monitoring of the insurance risk management compliance with the internal risk management policies and legal standards, such as ISO 31000, NAIC, NICB, SOX, IFRS17, NYDFS (for NYC), GDPR and Solvency II (for the EU), IA and SAMA (for the KSA), HIPAA (for health insurance), and more.
• AI-based detection of employee fraud and non-compliance.

• Automated reporting of the required insurance KRIs, risk trends, risk management activities, compliance, and more (by period, location, insurance portfolio, etc.).
• Customizable report templates for various insurance risks: underwriting risks, claim fraud risks, financial risks, regulatory risks, etc.
• Support for multiple visualization types for risk analytics, including interactive tables, risk matrices, risk trees, risk heat maps, multi-dimensional charts and graphs.
• Configurable risk dashboards for various roles: financial analysts, HR experts, compliance auditors, etc.
• Scheduled and ad hoc risk report submission to senior management and legal authorities.