Probability of loss / chance of lossĀ For example, leaving the keys inadvertently in the car door increases the risk of theft and the reverse situation is the closure of the vehicle and leave it in the garage by a guard will reduce the risk of theft, that there are degrees of danger (degrees of probability of loss) In the first case we say that The risk is high in the sense that the degree of danger is high or the probability of theft is great and in the second case we say that the risk is low in the sense of the degree of danger is low or the possibility of theft is low

When we say that the insured has the risk or the risk, it means that the future consequences will be borne by the potential loss.

By comparison we say the risk of fire, the risk of war, the risk of earthquake, the risk of unemployment but we say the opportunity to win the chance of success in the exam the probability of profit.

## What are the insurance risks?

* Risk means the possibility of loss and the probability of loss means uncertainty of the occurrence of loss or the occurrence of an undesirable future and uncertainty means the suspicion that a particular accident will occur in the future or not because we do not know the unknown and do not know what will happen in the future meaning that When making sure that the loss is impossible, there is no risk and, when certain losses are determined in the future, there is no risk even if this loss is in millions of dollars.

Probability is a measure of the degree of probability of an accident and is expressed as a percentage ranging from zero to the correct one but does not include the zero or the correct one, because when the probability is equal to zero, there is no probability of loss means there is no risk (for impossibility) When the probability is equal to the correct one, it means that the loss (“confirmed loss”) is a risk, even if the loss is in millions of dollars.

## Personal probability

* Personal probability is a possibility that is subject to an individual’s belief, for example, gambling or buying lottery tickets because he thinks he is lucky or believes he can manipulate gambling.

## Personal risk in insurance

Personal risk is the degree of uncertainty that an individual perceives, so they differ from person to person for the same situation or potential event, for example two people drank alcohol at the party of the first person drove his car drunk and returned to his home and the second person afraid to catch him The police and personal danger is influenced by past experience. Suppose the second person has been arrested by the police in the past. For example, two people want to cross the road. The first person breaks the traffic sign, believing that nothing will happen, and the second person waits for the green light to cross the road for fear of accidents.

## The objective probability of an accident occurring

Objective Probability is the possibility of an accident that can be calculated by means of deductive reasoning or the method of inductive reasoning.

In the method of deductive mental analysis, we use true and true general information about things to arrive at a particular conclusion. For example, if all people die, George will die because of people and the example of deductive probability is when we throw a coin, 1/2 means 50% because the coin has only two faces (face and face in writing) and when we throw the table cube (in the dice game), the probability of the face having three points is 1/6 means 17% because the table cube has 6 points Only one face has three dots.

## insurable risk

Most of the insurable risks can not be computed using the deductive reasoning method, so the method of reasoning is used in predictive and probabilistic situations.

In the method of inductive reasoning we use special facts or daily observations to form a general principle or a general conclusion. An example of inductive probability is that the insurance company uses insurance statistics for previous years to determine the probability of loss in the future Suppose that the insurer insured over 10,000 homes in previous years Losses in previous years were 110 homes damaged in the first year, 90 homes damaged in the second year, 80 homes damaged in the third year and 120 houses damaged in the fourth year and took the average of these numbers and 100 houses damaged annually and divided this number on the number of (1) the probability of loss is the product of dividing the number of expected losses (or average actual losses in the past) by the number of units at risk (the number of possible losses).

The insurance company predicts that on average 1% of insured homes are damaged as a result of fire incidents annually and we say on average that it is very rare that 1% of insured houses will be damaged exactly every year. Actual loss should deviate from expected loss.

* If the number of houses insured in a given year equals 10,000 houses and the probability of loss is 1%, the expected loss is equal to 100 damaged houses, but in this particular year, 110 houses may be destroyed. Therefore, there is a deviation of the actual losses from the expected losses is 10 houses. 10% This is the objective risk / degree of risk, which is the distribution of the deviation, which is 10 houses damaged on the expected loss of 100 houses damaged. In theory, insurance scientists assume that the deviation equals the square root of the expected loss for Example if the number of houses insured on (The square root of 1,600 houses)) and the objective risk is equal to 2.5% ((40 1600 1600)) and the objective risk in the inverse relationship with the square root of the number of units (Houses) at risk, the objective risk decreases as the number of vulnerable units (houses) increases. When the square root of ten thousand insured homes is 100, the objective risk is 10%. When the square root of 160,000 houses is insured, Objective risk 2.5%.

This is consistent with the law of large numbers, which states that the higher the number of units at risk, the closer the actual losses are to match and offset the expected losses, the greater the objective risk decreases and the lower the number of units at risk, the more actual losses will deviate from Expected losses are a significant deviation.