Actuarial science deals with probabilities and future events with the help of Statistical methods. Statistics help define, analyse and solve financial scenarios or risks that firms and companies need to manage or avoid. For example, with the help of statistics, insurance companies are capable of forecasting the probabilities of an event or anomaly occurring, thus being able to prepare the funds for paying claims.
Further, actuarial science mainly focuses on the financial impact of various events. For instance, using probability analyses, the risk of an accident can be quantified in order to reduce its impact or prepare a contingency plan. Various other mathematical and statistical models are employed for various insurance policies and investments.
Initially, actuaries used to depend on deterministic models but with the growing adoption of technology, there are many advanced statistical tools that have replaced drawing tables and jotting down formulas manually. Actuarial science also heavily depends on economics, thus making statistics even more essential.
In finance, statistical data helps in making business-driven decisions. For example, a company can assess the production efficiency of its factory in order to find out if it can reach its business goals during the current fiscal year. Statistical models and tools have been helping investment firms make good investments for decades as well.
Financial analysts use statistical techniques and software for analysing data and identifying trends, patterns and anomalies in the market or financial transactions. From credit analytics to risk management, statistics is used for everything. Let’s learn more about how important the domain of statistics is for finance and actuarial science.
Statisticians in finance and actuarial science
At the macro level, statisticians help in assessing the financial condition of a country and its economy. Other key information such as economic growth, market fluctuations and various substrata of global performance can also be analysed using statistics. Notably, statisticians are required for all these kinds of analysis.
Government bodies and agencies around the world employ hundreds of statisticians in order to help them analyse financial markets, national expenditures and the economy of the country in general. Whether it is studying a country’s past economic data and mapping out how the future of a certain sector (for example, IT or data analytics) will look like or planning a budget for a project that will take 10 years to complete, statisticians are required everywhere.
Let’s take the example of a project which involves distributing meals for the needy. The government body in charge would need to allocate the budget for this. In order to not fall short of funds during times of crop shortage or price hikes, statisticians can help the agency prepare beforehand by taking these kinds of abnormalities into account.
The same can be said for industrial projects at the micro level. A statistician can analyse the total number of breakdowns for all the machines in the factory and determine the annual probability of these machines needing repair and a change in specific parts. This way, the various repair costs can be estimated and accordingly adjusted with the operational cost. This will help companies prepare the additional funds needed to run their machines and price their final products better.
Statisticians also find themselves in the position of credit analysts for money lending organisations or banks. By analysing a person’s credit and payments history, one can determine the maximum loan amount a customer can be allowed to borrow. Statisticians are also able to analyse the financial performance of investments and businesses in order to determine how profitable they will be.
Similarly, statisticians also find themselves in the job roles of business analysts, financial analysts, market analysts and cost estimators. All these roles focus on advising companies or clients about markets, customers, investments, assets and costs. All of these tasks require statistics in order to evaluate how profitable an investment will be or how much will be the long term cost.
Further, statisticians are tasked with making predictions and forecasting market or stock movements. Without statistics, it is almost like being blind in the world of finance. Especially with competitors using data-driven business strategies, one cannot afford to not utilise statistical mathematics.
For actuarial science, statisticians are tasked with evaluating liabilities and investments. The most common application of statistics in this sector is in risk management and for financial schemes such as pension plans and life insurance. Most of these financial products along with annuities are prepared with the help of skilled statisticians.
In life insurance, the longevity of the population is used along with life tables and compound interest in order to build annuities and insurance plans. Probability theory and other statistical concepts are used for mortality analysis and the general insurance of any asset. For example, companies use statistics to study the rate of accidents in order to plan premiums and cover their annual expenditure.
Importance of statistical tools
Various statistical tools such as SAS and custom-built ERP (enterprise resource planning) systems are used for financial analytics. Even software (such as Excel) offering statistical functions are used for various financial tasks and report generation.
Here are the statistical concepts that are used in financial tools and analytics software:
- Frequency distribution
- Relative and cumulative frequency
- Weighted average return
- Median, geometric mean, arithmetic mean
In the case of actuarial statistics, many tools that let you apply regression models and Bayesian statistics are used. For these kinds of applications, programming languages such as R and IDEs like RStudio are extensively used by statisticians. Actuaries are expected to employ statistical distribution, data summarisation, statistical inference and many other statistical functions using SAS, MATLAB, Excel, IBM SPSS Statistics and Prism.
The final word
When it comes to actuarial science and finance, knowledge of foundational statistical concepts is absolutely necessary. This is especially true when companies or clients depend on you to help them make the right business or investment decisions. Programming languages such as R are also gaining more popularity for financial applications and general analytics. R is a programming language meant for statisticians, thus, making the financial and actuarial sectors more desirable for individuals pursuing statistical studies.
Also Read: Ten Investment Ideas For Beginners
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