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# Statistical Functions As Risk Measurement Tools

By comparing two potential factors in an investment or the profit or loss of a project, risk measures can be identified by applying statistical methods. Notably, statistical methods have been tried and tested to offer investors and financial geeks insights on the return to investment or profitability, even before it kicks off. With previous or identical data being in place, statistical methods can be applied in a financial situation to tell of the potential risks and profitability score the project is set to acquire and make. Premium Acceptance can forecast its profitability and loss ratios through considerable statistical measures such as the Sharpe ratio, standard deviation, alpha, and beta. With data in hand, the organization can be better positioned to deliver to its capacity without much fear of making losses.

Statistics is core in the risk assessment process since the probability and impact of risk are assessed, and a well-composed assessment is done and delivered. For instance, in our case, with the aid of probability in measuring the rate of trouble that might hit our business, we are well indicated of the frequency and likelihood of each risk happening. By so doing, we are well aware and seek ways to curb the menace in case it happens. Moreover, if a change occurs, which in our case, our losses exceed our premium carried, we can predict the rate at which the impact will happen. The frequency and effects of risk can also be expected using statistical methods, making it easy to make a utility assessment in case of a negative impact. In our case scenario, however, we can apply the value at risk measurement in a bid to identify our actual and maximum loss for a while in case of an undefined risk (Tursoy, 2018). The standard deviation would also work in our favor since it would point out the rate and percentage of risk expected by measuring and calculating our insurance carried against the insurance premium paid.

An effective statistical method that can be applied to Premium Acceptance finances and give an accurate risk ratio is the probability measure of risk. We can identify an underlying risk and highlight its overall effect on our profits through the probability measure. Moreover, we can tell the best mitigation practice to suppress the risk from occurring frequently (Tursoy, 2018). For instance, if as a company we make a fifty percent profit and a 5 percent profit in the premium of a car, and make a ten percent profits and 35 percent loss in another bonus, and lastly make a profit of thirty percent and loss of sixty in the last premium, then the loss ratios can be identified. Notably, the available statistics can easily predict the loss ratio in the fourth month, resulting in a 7/20. An additional statistical measurement that can be effectively applied is the standard deviation (Page, McKenzie & Higgins, 2018). In its concept, the positive square root of squares under review of variations of values of a variable is identified and used to measure the risk levels. In our case scenario, the standard deviation can be determined if the net profits and net losses are subtracted from each other, then the standard deviated. Analyzing three past months can lead to an actual figure and trend analysis of possible risks in the upcoming months, holding all factors constant.

The company aims to identify the possible risks by measuring different types and natures of profits against the losses. A perfect example of a risk tool that would populate data and show organizational risks is the value at risk tool. The level of risk associated with the nature of our business will be assessed by the tool since it will be able to measure the maximum level of losses we can incur for a specific period (Page, McKenzie & Higgins, 2018). We shall be better positioned to identify and measure a notable degree of confidence and threshold of the maximum loss through the conditional value of the risk tool in place. A shortfall in a financial tail is notably identifiable through the dependent value at risk tool of statistics. An additional tool that will be effective to our business operations through its ability to measure the risks is the beta tool. By applying the beta tool, we as a company, specifically the risk department, shall be in a perfect position to weigh the systematic risk available in our sector and its overall effect on the entire company.

Making decisions based on data is not only a factual method of decision-making but is also a method of accurate prediction of the company’s financial future. With statistics applied, an organization can e in a comparative place, analyzing data from the recent past and predicting its economic future. If our organization assumes the role statistics and its data interpretation role plays to its survival, a notable adverse effect will be experienced, especially on its risk. Notably, hidden risks will not be covered, exposing the organization to financial misappropriation and mismanagement (Figlewski, 2018). Additionally, since there will be no methods to gather and analyze data, decisions will be based on qualitative data rather than quantitative data present. The company’s overall profit and loss ratio will turn side as an effect, and the losses increase due to a limited set of quantitative data and analysis. Above and beyond, due to the lack of accurate predictions, the company is likely to turn and drive to a loss and even a possible closure due to enormous losses.

### References

Figlewski, S. (2018). Risk-neutral densities: A review. Annual Review of Financial Economics10, 329-359.

Page, M. J., McKenzie, J. E., & Higgins, J. P. (2018). Tools for assessing risk of reporting biases in studies and syntheses of studies: a systematic review. BMJ open8(3), e019703.

Tursoy, T. (2018). Risk management process in banking industry.

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