Update README.md

README.md

@@ -38,7 +38,7 @@ One of the primary uses of this dataset is to apply the chain ladder method, a t
 Bayesian Framework(Innovative methods recommended): <br>
 The dataset can be utilized within a Bayesian framework to enhance the predictive modeling process. A Bayesian approach allows for the incorporation of prior knowledge or expert opinion into the statistical models, updating these beliefs with data from the dataset to generate a posterior distribution of the expected claims. This method is particularly useful when dealing with complex systems or when the available data is sparse or contains a high level of uncertainty.
 
-1. In the context of the LTC aggregated dataset, Bayesian hierarchical models can be applied to account for multiple levels of variability, such as between different policyholders, across various regions, and over time. These models can also help in understanding the effects of policy features and policyholder characteristics on the likelihood and timing of claims, providing a deeper insight into risk factors.
+1. Regarding this LTC aggregated dataset, Bayesian hierarchical models can be applied to account for multiple levels of variability, such as between different policyholders, across various regions, and over time. These models can also help in understanding the effects of policy features and policyholder characteristics on the likelihood and timing of claims, providing a deeper insight into risk factors.
 
 2. Another significant use of the dataset in the Bayesian framework is the development of predictive distributions for various risk metrics. These could include the probability of claim terminations due to death or recovery, the expected number of claims within certain diagnosis categories, or the expected claim durations.
 
@@ -129,15 +129,26 @@ I encourage all users of this dataset to notify me directly if they encounter an
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+### Bias
 
-[More Information Needed]
+- **Selection Bias**: The dataset primarily sourced from insurance companies participating with the Society of Actuaries may not represent all types of LTC insurance providers, particularly smaller or regional companies that might have different claim patterns or policyholder demographics.
+- **Reporting Bias**: There might be inconsistencies in how data is reported across different companies, affecting the uniformity and comparability of the information. For instance, the threshold for defining a claim or the categorization of terminations could vary, leading to potential biases in analysis.
+- **Survivorship Bias**: The data might inherently focus more on policies that have led to claims or terminations, overlooking the ones that are still active without incidents. This could skew the understanding of risk and the overall policyholder experience.
 
-### Recommendations
+### Risks
+
+- **Privacy Risk**: Despite the efforts to anonymize the data, there is always a risk, however minimal, that individuals could be re-identified through a combination of variables, especially in smaller, more homogeneous datasets.
+- **Misinterpretation**: Without a deep understanding of the intricacies of insurance data and the specific context of LTC products, there's a risk that the data could be misinterpreted, leading to inaccurate conclusions or policy decisions.
+- **Missing Data Risk**: Given that some data points are missing, analyses could be biased if the missing data is not random. This can lead to inaccurate conclusions.
+
+### Limitations
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- **Temporal Relevance**: The dataset, while comprehensive, captures data up to a certain point in time. The rapidly evolving nature of the LTC insurance market and changing demographics might limit the relevance of historical data for predicting future trends.
+- **Complexity of LTC Products**: LTC insurance products are complex, and the dataset might not fully capture the nuances of different policy features, benefit triggers, and underwriting practices, which could affect the analysis.
+
+### Recommendations
 
-Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.
+[TBD]