Bayesian Risk Model Net Benefit Calculation
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Solution Overview
Problem
Existing actuarial-based methods for risk modeling in the insurance industry are less effective with limited data, failing to accurately estimate future losses and unable to assess individual risks effectively, and lack a method to determine the value of risk assessment surveys.
Innovation Solution
A method to determine the net benefit of obtaining survey data for individual risks using a Bayesian predictive model that combines historical data, current data, and expert opinion, incorporating a revenue model and probability distributions to quantify the value of additional information from surveys, allowing for informed decisions on conducting surveys.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional actuarial methods are used for risk modeling, then they work effectively with large amounts of data, but they become less effective and inaccurate when data is limited
Solution Approach 1:
The patent changes the fundamental parameters of risk modeling from traditional actuarial methods to Bayesian predictive models. This allows the system to effectively utilize limited data by incorporating prior knowledge and updating beliefs based on observed data, rather than relying solely on large datasets for curve fitting. The Bayesian framework enables accurate predictions even with small sample sizes by leveraging expert opinion and historical information.
Solution Approach 2:
The patent introduces expert opinion as an intermediary element in the risk modeling process. By incorporating expert judgment alongside limited data points, the system creates a more robust predictive model. The expert opinion acts as a mediator that fills gaps in the data, allowing the model to function effectively even when direct observational data is scarce.
2Measurement precision
If risk assessment surveys are conducted to collect data on individual risks, then predictive accuracy improves, but survey costs increase
Solution Approach 1:
The patent performs preliminary calculation of the expected value of information before actually conducting the survey. By estimating how much additional information from a survey would improve predictive accuracy and translate into financial benefit, the system can determine in advance whether the survey cost is justified. This preliminary assessment prevents unnecessary surveys from being conducted.
Solution Approach 2:
The system establishes a feedback loop where survey results are used to update the risk model, and the improved model then informs future survey decisions. The expected value of information calculation provides feedback about whether additional surveys are warranted, creating an adaptive process that optimizes the balance between data collection costs and predictive accuracy benefits.
3Quantity of substance
If surveys are conducted on all risks, then comprehensive data is collected, but unnecessary costs are incurred on risks where surveys provide no value
Solution Approach 1:
The patent applies local quality by tailoring the data collection strategy to individual risks based on their specific characteristics and the value of additional information for each risk. Rather than uniformly surveying all risks, the system identifies which risks would benefit most from survey data and directs resources there. Each risk is evaluated individually to determine the expected value of information, allowing customized data collection approaches.
Data Source
AI summary
A method is disclosed for determining the prior net benefit of obtaining data relating to an individual risk in an insurance portfolio, via a survey or similar procedure. A risk model is developed at the individual risk level for mathematically estimating the probability of expected loss given a set of information about the risk. The risk model is incorporated into a profitability model. A probability distribution relating to the type of survey information to be obtained is developed, which is used for determining the gross value of obtaining the information. The method produces as an output a quantitative estimation (e.g., dollar value) of the net benefit of obtaining survey data for the risk, calculated as the gross value of the survey less the survey's cost, where the benefit of the survey relates to a quantitative increase in predictive accuracy resulting from incorporating the survey data into the predictive model.