Videos

Preparing data for Catastrophe Risk Models
QuanTemplate provides flexible tools to automate data preparation for catastrophe risk models and other downstream systems.
In this video we see how a QuanTemplate pipeline transforms a property Schedule of Values to a typical risk model format.
Video features
  • Join
    reference data
  • Map
    to model schema
  • Format
    data for model compatibility
  • Machine Learning
    to automate processes. The more data that runs through QuanTemplate, the smarter the system becomes.
Data Integration Demo
QuanTemplate Integrate is a suite of self-service data preparation and integration tools, enabling those who understand the data to control the data.
This video shows a full workflow demonstration of a binding authority scenario: harmonising disparate data sources from multiple coverholders, then analysing and sharing that data.
Video features
  • Import
    data in range of formats and define the structure
  • Map column headers
    to a common schema aided by
    ML-powered Smart Matching
  • Map data values
    such as company names to a consistent standard
  • Join Premium and Claims data
    to precisely determine exposure
  • Join external data
    to enrich bordereaux
  • Cleanse invalid data
  • Add calculations
    to determine profitability
  • Analyse the data
    with QuanTemplate’s business intelligence tools to gain actionable insights
  • Automate Solvency II reporting
    including data exports for Annual Solvency Returns
  • Share data
    with regulators and following underwriters
Business Intelligence and Analytics Demo
QuanTemplate Analyse is a self-service BI environment, allowing business users to instantly drill into their data and take actions to optimise their portfolio.
This video shows an insurer portfolio optimisation scenario, drilling into coverholder data to establish top and bottom performers and assess accumulation and clash risk.
Video features
  • Pivot tables
    to instantly segment and filter
  • Equation editor
    to calculate loss ratios
  • Graphing library
    to visualise trends and identify outliers
  • Policy-level analysis
    to drill into claims data
  • Geoplotting
    to reveal aggregation and clash
  • Integrating new data
    from additional coverholders