Data Analysis and Statistical Methods for Parametric Insurance
Expert-defined terms from the Certified Professional in Parametric Insurance course at Stanmore School of Business. Free to read, free to share, paired with a globally recognised certification pathway.
Actuarial Analysis #
The practice of determining the financial consequences of risk in insurance, using mathematical and statistical methods. In parametric insurance, actuarial analysis involves estimating the probability of a specific parametric trigger event and calculating the corresponding payout.
Basis Risk #
The risk that the actual loss experienced by the insured will differ from the parametric trigger event used in the insurance contract. This occurs when the parametric trigger does not perfectly correlate with the insured's actual losses.
Data Analysis #
The process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. In parametric insurance, data analysis is used to understand the underlying risk factors, estimate the probability of trigger events, and calculate expected payouts.
Data Mining #
The process of discovering patterns and knowledge from large datasets using statistical and machine learning techniques. In parametric insurance, data mining can be used to identify trends and relationships between trigger events and insured losses.
Extreme Value Theory (EVT) #
A branch of statistics that deals with the analysis of rare events or extreme values. In parametric insurance, EVT can be used to estimate the probability of extreme weather events, such as hurricanes or floods, and calculate corresponding payouts.
Generalized Linear Model (GLM) #
A statistical model that allows for response variables that have error distribution models other than a normal distribution. GLMs are used in parametric insurance to model the relationship between trigger events and insured losses, taking into account the specific error distribution of the data.
Historical Data #
Data collected over a period of time that reflects past events and trends. In parametric insurance, historical data is used to estimate the probability of trigger events and calculate expected payouts.
Indemnity #
Based Insurance: A type of insurance that provides a payment based on the actual loss experienced by the insured. In contrast, parametric insurance provides a payment based on a predefined trigger event.
Machine Learning #
A subset of artificial intelligence that uses statistical and mathematical models to enable computers to learn and improve from data without being explicitly programmed. In parametric insurance, machine learning can be used to identify patterns and relationships between trigger events and insured losses.
Parametric Trigger #
A specific event or threshold used in parametric insurance to determine whether a payment is due. The trigger event is defined in advance and does not require proof of actual loss.
Probability Distribution #
A mathematical function that describes the probability of different outcomes in a random experiment. In parametric insurance, probability distributions are used to estimate the likelihood of trigger events and calculate expected payouts.
Risk Analysis #
The process of identifying, evaluating, and prioritizing risks to make informed decisions and manage potential consequences. In parametric insurance, risk analysis involves estimating the probability and potential impact of trigger events and calculating corresponding payouts.
Simulation Modeling #
The use of computer-based models to simulate real-world scenarios and predict outcomes. In parametric insurance, simulation modeling can be used to estimate the probability of trigger events and calculate expected payouts.
Stochastic Process #
A mathematical model that describes a sequence of random variables over time. In parametric insurance, stochastic processes can be used to model the probability of trigger events and calculate expected payouts.
Statistical Modeling #
The use of statistical methods to create mathematical models that describe and predict real-world phenomena. In parametric insurance, statistical modeling is used to estimate the probability of trigger events and calculate expected payouts.
Subjective Probability #
A probability that is based on personal belief or opinion, rather than objective data. In parametric insurance, subjective probability can be used to estimate the likelihood of trigger events when objective data is scarce or unavailable.
VaR (Value at Risk) #
A statistical measurement that quantifies the risk of losses in a portfolio of assets over a specific time horizon. In parametric insurance, VaR can be used to estimate the potential impact of trigger events and calculate corresponding payouts.
Weather Derivatives #
Financial contracts that are based on weather variables, such as temperature or precipitation. Weather derivatives can be used in parametric insurance to transfer weather-related risks to a third party.
Weight of Evidence #
A statistical method used to evaluate the strength of evidence in support of a particular hypothesis. In parametric insurance, weight of evidence can be used to estimate the probability of trigger events and calculate expected payouts.