Insurance

             INSURANCE

Insurance involves financially indemnifying an insured against risks due to life uncertainties or undertaken activities. It broadly includes:

1. General Insurance for homes or commercial establishments
2. Life Insurance for individuals or groups
3. Medical Insurance for individuals or groups

Actuarial science measures these risks, but several commercial aspects lie outside its scope, such as:

1. Stress Test Modeling
2. Mix and Match of Coverage Codes for Improving Loss Ratios
3. Persistency Rates of Customers
4. Claim Processing Time
5. Business Loss Due to Various Reasons

Data science can create valuable insights for managing these aspects, with models often tailored to specific geographical locations. Here are some key use cases:

Stress Test Modeling

Stress Test Modeling helps insurance companies assess asset and investment performance under adverse scenarios, such as:

1. Changes in asset and liability values
2. Solvency projections
3. Interest rate shocks

Loss Ratios

While actuarial methods typically calculate loss ratios, data science can help mix low-risk coverage’s with high-risk ones to optimize these ratios. This approach can transform loss-making products into profitable ones.

Claim Processing Time

In a competitive insurance market, fast claim processing is crucial for maintaining customer persistency. Data science can monitor and improve claim processing times, helping insurers set and achieve goals.

By highlighting improvement areas, data science aids insurance executives in taking remedial measures, boosting the company’s profitability and productivity, and resulting in a better return on investment.