Analytics for Insurance

Data Science & Insurance

Analytics for Insurance

Insurance is a diverse set of products and each one warrant a details understanding of the products and features. Two broader types of insurance products are Life Insurance and General Insurance or Property and Casualty (P&C) Insurance.

Life Insurance

In Life Insurance, risk cover is associated with life style and life expectancy for an insured person. The risk is transferred to an insurance organization. The insured person pays a premium to the insurance provider for accepting the risk on his behalf. If eventuality happens, the risk is realized and the beneficiary get the money (called sum insured).

For insurance products and plans, insurance underwriters have been used broad categories to identify risk of a category and defined the premium. Some of the common classification variables were age, and gender. Due to demand from the customers and increased competition, the insurance underwriter have become sophisticated with segmentation and pricing. Life style based segmentation is used for pricing decisions. Mortality can be impacted by family & personal medical history, occupation type and personal physiological attributes. The insurance providers consider these factors in giving preferred discounts to normal premium value.

Analytics for policy risk underwriting and premium estimation has been used for a long time. But focus on analytics has also grown for customer and operational analytics in the recent years.

General Insurance or Property & Casualty Insurance

Property and Casualty insurance protects against losses to home (Home Insurance), vehicle (car or motor insurance), and pet animal (Pet Insurance). There are a few other types of insurance such as travel insurance, flood insurance, and health insurance.

Significant P&C Insurers are expecting to increase data and analytics spend. Some of the common dimensions involved in Insurance Analytics are Customer Analytics, Underwriting Models, Customer Switcher or Churn Analytics, Claim Forecasting, Agent & Advisor Analytics, Claim Fraud Modeling and Sales & Marketing Effectiveness.

Insurance Analytics across customer engagement cycle:-



  • Insurance Underwriting - Mortality rates modeling
  • Acquire cost effectively
  • Agent Segmentation and Productivity Enhancement
  • Agent Effectiveness and reward
  • Pricing appropriately
  • Claim Modeling & Forecasting
  • Agent Recruitment and Retention


  • Customer retention
  • Cross sell
  • Operational Efficiency
  • Premium Payment Analysis
  • Renewal and Relapse Modeling


  • Complaint Resolution Analytics
  • Claim Processing
  • Fraudulent Claim Modeling
  • Claim Efficiency & Effectiveness for reduced losses