Sample Selection From Large Data For Audit

Different risks in insurance service are covered by terms and conditions in Insurance Policies. In the event of claims arising they are managed or settled accordingly. Auditing the underwriting, policy management, and claims management procedures is a common practice. To ensure that the organization follows healthy practices. It is not a good idea to audit every record because there are often many policies and claims. Sampling techniques can be used to reduce audit volume without compromising quality.

There are many areas where sampling techniques are used. Pathologists collect blood samples from the patient and then test it to determine how much blood is present. To determine the whole lot of rice, we boil a few grains of rice and test them. This method leads us to the right conclusion, we know. Pathological tests can be performed on blood with uniform properties. The rice is also uniformly heated when it is boiled. This uniformity is why a small sample is enough to reach the right conclusion. Rice that is cooked with less water than is necessary for uniform heat flow will not cook evenly. If this happens, it is possible to make a wrong conclusion by testing a few grains and making an opinion about the whole lot. Similar to above, if the ingredients being combined are not identical, we will need more samples in order to reach an acceptable conclusion.

Because of the inherent nature of insurance, data related to different functions can have differences. Auditing is more expensive if the sample size is too large. However, keeping it small can lead to an invalid or incorrect conclusion. It is therefore important to select the right sampling technique and size.

It is easy to use sampling techniques efficiently. It is difficult to use sampling techniques effectively when we decide the sample size before knowing the data. These are the four factors that determine sample size:

1. The size of the population: To draw conclusions about the claims management system, we need to include all claims that were filed or denied. If the rest of the factors are not changed, the sample size will be larger.

2. Variance is the measurement of data variation. We can make an accurate assessment of the whole lot of items if there is not any variation by simply checking one item. If there are variations, we will need to examine more. The sample size will be larger if there is more variation. Both standard deviation and variance are measures of variation in data. They both represent the same property.

3. Confidence Level: Let’s say we audit 10 cases of claims and discover that 3 cases did not follow a business rule. This number is 6. We also found it in 20 cases of claims audited. This could lead us to conclude that the rule was not followed in 30% of cases. How confident would we be in this conclusion? The confidence level in the conclusion will increase if we audit more cases with similar results. The sample size is also important if the required confidence level is high.

4. Accuracy: The larger the sample size, the higher the accuracy.

It is obvious that sampling techniques and sample sizes cannot be determined without understanding the data’s nature. Because these data are generated by various operations within the company, auditors cannot make any changes to the population or variance. The sample size needed can be very large if the accuracy and confidence levels are very high. To keep costs and time down, it is best to keep these values reasonable.

It is a good idea to conduct a small initial study in order to get a better understanding of the population size, variance, and the basic nature of the data. Next, use the data to calculate the sample size and then conduct the detailed study. The sample must be representative of the entire population. This is why random numbers are generated and then associated with actual records using statistical distribution of data.