The International Association of Insurance Supervisors (IAIS) has developed guidance and standards for internal models. The main challenge for constructing internal model for developing countries is shortage of data. Therefore, data needs to be simulated and expert assumptions about underlying model parameters should be considered.  This paper discusses models to evaluate underwriting risk, reinsurance credit risk, market risk and operational risk for developing countries.

The aggregate claim S(t) in underwriting risk section itself is a random variable, as the aggregate risk is a composite of two random parameters, namely the number of claims and their severity. The distribution of S is obtained from the distribution of N and the distribution of X. In practise, the frequency and severity of claims are modelled separately, as this method is advantageous in many cases due to several following factors:

• The expected number of claims depends on the number of written insurance policies
• Changes in economic variables, as inflation and additional claims inflation, are reflected in the losses X_i, as the bigger inflation the bigger are the average losses.
• The shape of the distribution of S is in compliance with the shape of distributions of N and X. It is very useful in determining the details of policies and etc
• Generally, a precise and flexible model can be designed by investigating severity and frequency distributions separately.

The main aim in this section is to find distributions of claim numbers and severities  for different type of insurance contracts.

One particular case of credit risk reinsurer’s default risk. Unlike bank deposits or loans, the obligations of reinsurers are extremely volatile and depends on aggregate claims of insurer. Therefore, .in order to model Reinsurance Credit Risk it is necessary to model the amount of recoverable, loss given default and exposure from reinsurer which depends on the type of reinsurance contract. Recoverable, loss given default and exposure are random since they also depend on the number and severity of underlying claims.There is no much data on operational losses, therefore operational risks simulation methods will be presented.

Market risk estimation model based on information about main types of insurer's invertments will be presented. The ways to find optimal asset allocation strategy will be discussed.