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Custom Insurance Actuarial Software

ScienceSoft combines 12 years of experience in insurance software development with 35 years in data science to design secure and high-performing solutions that digitally transform insurance actuarial workflows.

Custom Insurance Actuarial Software - ScienceSoft
Custom Insurance Actuarial Software - ScienceSoft

Insurance Actuarial Software: The Essence

Insurance actuarial software is aimed to streamline actuaries’ workflows across risk modeling and analysis, loss reserve planning, policy pricing, and disclosure reporting. Such solutions provide a comprehensive toolkit for model design and management, automate insurance data aggregation and actuarial calculations, offer multiple data visualization options.

Custom insurance actuarial software can rely on advanced data science techniques to enable the creation of complex and unique models and support instant calculation of business-specific parameters. It can introduce AI-based automation of actuaries’ tasks. Custom solutions offer easy model evolution, provide robust security of actuarial data, and simplify compliance with all required regulations.

  • Necessary integrations:
    • Corporate systems: an underwriting system, a claim management system, policy administration software, accounting software, CRM, etc.
    • Third-party data sources: internal systems of credit rating bureaus, medical information bureaus, police administration, telematics providers, etc.; asset tracking systems of business clients; smart wearables of the insured persons; social networks.
  • Implementation time: 9–15+ months for a custom insurance actuarial system.
  • Development costs: $200K–$600K+, depending on solution complexity. Use our free calculator to estimate the cost for your case.
  • ROI components: more than 20x faster actuarial modeling, an opportunity to reduce human resource utilization by 25%, more accurate insurance pricing and capital modeling resulting in improved financial performance and minimized risks.

Key Features of Insurance Actuarial Software

ScienceSoft helps its insurance clients create actuarial solutions with accurate business logic that smoothly automates even the most complicated, highly specific actuarial workflows. Below, our consultants share a list of features that lay the foundation for a robust insurtech actuarial system.

Support for diverse insurance actuarial models

  • By use case: risk models, pricing models, loss reserving models, etc.
  • By insurance type: P&C insurance models, life and annuity insurance models, flood insurance models, etc.
  • By analysis method: statistical models and machine learning (ML) models, including deep learning models.
  • By forecasting period: Short-term and long-term models.
  • By currency: Single-currency (including cryptocurrency) and multi-currency models.
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Insurance actuarial model management

  • Creating custom insurance actuarial models with user-defined parameters.
  • AI-based recommendations on relevant parameters to include in actuarial models.
  • Automated conversion of the created models to customizable templates.
  • Building custom natural language formulas to calculate business-specific metrics.
  • Configurable hierarchies between different actuarial models.
  • Collaborative model creation and editing.
  • Automated model version control.
  • Real-time evaluation and monitoring of model biases and variances.
  • Notifications to actuaries about the areas of poor and superior model performance.
  • (for data scientists) Designing, training, tuning, and monitoring the performance of custom ML models.
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Insurance data intake

  • Real-time and batch aggregation of data relevant for insurance actuarial modeling, including:
    • Underwriting data.
    • Claim data.
    • Corporate financial performance data.
    • Data on the policyholder behaviour and the insured asset state.
    • Geospatial, geopolitical, demographic, and economic data.
  • Support for data in various formats: text, digital images, XML, PDF, recorded voice messages, streaming video, etc.
  • AI-based data pre-processing (structuring, cleansing, enrichment) and validation.
  • Centralized storage of the collected data.
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Diagnostic analytics

  • Rule-based trend analysis, including time series analysis.
  • Comparing the captured homogeneous data across user-defined periods and identifying performance trends for various insurance variables.
  • ML-powered analysis of the collected insurance big data and automated identification of:
    • Dependencies between multi-dimensional variables, e.g., region-specific mortality rates and loss amounts under the life insurance products, climate changes across the flood hazard areas and claim severity.
    • Quantitative impact of particular variables on other parameters.
    • Key change drivers for actuarial models.
    • Change patterns for insurance parameters.
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Risk modeling and analysis

  • Scenario modeling and what-if analysis for perils and potential loss events, e.g., property damage due to natural or human-made disasters, health issues due to seasonal diseases.
  • Individual and pooled risk modeling.
  • ML-powered predictive risk analytics and probability assignment to different scenarios.
  • Automatically calculating the risk exposure, loss amount, loss ratio, profitability, and other parameters under user-defined scenarios.
  • Automated Monte Carlo simulations to quantify the impact of external uncertainty factors:
    • Accidents involving the insured persons.
    • Unforeseen geopolitical events (wars, acts of terrorism).
    • Changes in regulatory compliance requirements, etc.
  • Multi-party scenario testing within the same actuarial risk model.
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Loss amount forecasting

  • Automated calculation of the expected loss amounts (by period, location, customer, portfolio, insurance product, etc.) based on:
    • Existing liabilities (including reported but not settled losses).
    • Historical claim frequency and severity.
    • Past claim payouts and expenses related to claim investigation and settlement.
  • Trend-based calculation of loss development factors (LDFs).
  • Rule-based recalculation of the ultimate loss amounts taking into account LDF.
  • ML-enabled prediction of incurred but not reported (IBNR) losses based on the analysis of:
    • Natural disaster location and severity (for parametric insurance).
    • Occupational hazards (for group health insurance).
    • Environmental practices and supply chain risks (for business liability insurance), etc.
  • ML-powered ultimate loss forecasting based on the analysis of historical loss amounts, loss event probability, claim cycle duration, and more.
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Loss reserving

  • Automated calculation of loss reserves under various reserving methods, including:
    • Distribution-free chain-ladder method.
    • Bornhuetter–Ferguson method.
    • Average cost per claim method.
    • Paid-incurred chain (PIC) method.
    • Separation method.
    • Bayesian method.
  • AI-based suggestions on the optimal loss reserves based on the analysis of historical and expected losses, premium and investment income, operational expenses, costs of unforeseen events, reinsurance coverage, and more.
  • Rule-based loss reserve discounting.
  • User-defined loss reserve thresholds.
  • Template-based creation of time-framed loss reserve budgets (at the company, region, and insurance product levels).
  • Automated budget adjustment as the new relevant data appears.
  • Real-time monitoring of the current vs. planned loss reserve budget utilization.
  • Configurable notifications about the current reserves nearing or dropping below the pre-set limits.
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Insurance price modeling and optimization

  • Scenario modeling for user-defined insurance pricing strategies, including usage-based auto insurance pricing and dynamic PAYL insurance pricing.
  • Rule-based profitability analysis for various pricing scenarios.
  • Side-by-side comparison of the financial outcomes for various insurance price levels to identify the appropriate premium thresholds.
  • Calculating personalized and segment-specific insurance premiums based on the requested policy type, coverage amount, insurance duration, overhead expenses, region-specific taxes, and profitability limits.
  • AI-based prescriptions on the optimal personalized insurance premiums based on:
    • Customer risks and profitability.
    • Demand elasticity.
    • Loss probability.
    • Expected investment income.
    • Reinsurance costs.
    • Competitors’ prices.
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Visualization and reporting

  • Support for multiple visualization types for actuarial analysis results, including:
    • Drill-down and drill-up dashboards.
    • Customizable loss triangles.
    • Multi-dimensional graphs.
    • Interactive tables and maps.
  • Configurable actuarial model visualizations for various insurance roles and business entities.
  • Automated actuarial reporting in compliance with the global and regional regulations, e.g., LDTI, IFRS17, NAIC, USQS, FATCA, Solvency II (for the EU), FRC (for the UK), etc.
  • Customizable templates for various actuarial reports: loss prediction reports, reserving reports, price recommendation reports, etc.
  • Scheduled and ad hoc report submission to internal stakeholders and regulators.
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  • Multi-factor authentication.
  • Role-based access control.
  • Data encryption.
  • End-to-end audit trail of user activities.
  • Compliance with SOC1 and SOC2, SOX, NYDFS (for NYC), GDPR (for the EU), SAMA (for the KSA), HIPAA (for health insurance), and other required insurance data protection standards.
  • AI-powered detection of actuarial fraud and non-compliance.
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Get Tailored Insurance Actuarial Software

ScienceSoft’s team is ready to design and develop a custom solution to boost the efficiency of your actuarial workflows, introduce accurate actuarial analytics, and simplify regulatory compliance.