Dynamic financial analysis (DFA) is a simulation approach that looks at an insurance enterprise's risks holistically as opposed to traditional actuarial analysis, which analyzes risks individually. Specifically, DFA reveals the dependencies of hazards and their impacts on the insurance company's financial well being such as business mix, reinsurance, asset allocation, profitability, solvency, and compliance.
In addition to projecting stochastic future economic scenarios through using scenario generators such as interest rate, underwriting cycle and jurisdictional risk models, DFA also links the scenarios with the financial models of the targeted insurance company that is being analyzed. Such models not only reveal the operation and the business structure of the company, but also uncover the dependencies among its business practices. Because DFA tries to account for every aspect of the company, it produces a vast amount of data. As a result, analyzing and presenting the outputs effectively is of great importance.
Objectives
DFA is used primarily by financial practitioners to manage profitability and financial stability (the risk control function of DFA) Not only do DFA users seek to maximize shareholder values, but they also try to maintain customer values. Furthermore, outputs from DFA could help managers identify strengths and weaknesses of the following areas. Business mix: estimates relative and absolute value of each line of business (e.g. premium and commission level) compared to the company's financial. Reinsurance: uncovers the structure of the company's line of businesses such as contract types, interrelation among contracts, and cost of reinsurance. Asset allocation: determines whether a company is taking on too much investment risk, which could be minimized through diversification of investments. Profitability: measures the profitability of the company's each line of business. Solvency: reveals liquidity problems, which are mismatches of cash flows that a company might experience if it did not have enough cash to immediately meet financial obligations. Compliance: assesses the likelihood of insurance regulators intervening the company's business due to change in regulations or deteriorating business operations. Sensitivity: explores the company's reaction to a change in strategies and economic conditions in the future. Dependency: uncovers dependencies of all kinds of risks that are hard to understand without a holistic modeling and analysis tool.
Elements
DFA consists of the following 3 parts: Scenarios, generating expected and extreme economic scenarios to assess the company's reaction to changes Business Models, quantifying the company's business models and uncovering the dependencies among them Analysis Presentation, presenting the analysis to the executives who make strategic decisions
Careful calibration is required to ensure the accuracy of the scenarios and the correlations among business models.
Scenario
The scenario generator must meet the following criteria: Generate individual risks while keeping track of their dependencies with one another and with time (e.g., an increase in the price of gasoline might lead to less driving mileages of automobile policy holders, thus leading to fewer car accidents). Produce both normal and abnormal behavior of the risk factors (e.g., a 1% change in S&P index is normal; a 40% change is extreme). Simulate stochastic scenarios, meaning that scenarios are not the same every time you run the analysis. Assign mathematical models that best imitate the behaviors of the risk factors. Such models could be found in, though not exclusively, actuarial science, finance and economic disciplines. Track the incurred losses and the development of the losses, specifically the cash flows of the company's operation. This may help managers recognize the need to implement better asset liability management strategies.
Interest rate generator
The interest rate generator is the core fundamental of DFA. Many sophisticated interest rate models were created in the effort to best imitate the real world interest rate behavior. Although none of the existing models are perfect, they have their own advantages and disadvantages. The following is a simple interest rate model used in a publicly access DFA model.
Cox, Ingersoll, and Ross (CIR) interest rate generator
The CIR interest rate model characterizes the short-term interest rate as a mean-reverting stochastic projection. Although CIR was first used to project continuous changes in the interest rates, it is also acceptable to use it to project discrete changes from one time period to another. Below is the formula.