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--- |
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license: apache-2.0 |
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tags: |
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- Actuarial Science |
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- LTC Products |
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size_categories: |
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- 100M<n<1B |
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--- |
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# Project 1 Proposal of the Long Term Care(LTC) Aggregated Dataset |
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KAO, HSUAN-CHEN(Justin) <br> |
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NetID: hk310 |
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## Dataset Details |
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The long-term care aggregated dataset, essential for conducting experience studies, is an extensive and valuable compilation of variables central to the analysis and prediction of long-term care (LTC) insurance products. This dataset integrates two critical files: one detailing claim incidence and the other capturing policy terminations. This merger is crucial for valuation purposes, enabling a holistic view of the insurance lifecycle. |
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The dataset features a broad spectrum of variables, from demographic information such as `Gender`, `Issue_Age_Bucket`, and `Marital_Status`, to more nuanced policy-specific details including `Premium_Class`, `Underwriting_Type`, and `Coverage_Type_Bucket`. Additionally, the termination component enriches the dataset with variables like `Claim_Type`, `Region`, `Diagnosis_Category`, `Claim_Duration`, `Exposure`, `Benefit_Expiry`, `Deaths`, `Recovery`, `Terminations`, and `Others_Terminations`. These elements offer insights into the reasons for policy cessation, whether due to the policyholder's death, recovery from the condition leading to the claim, or other factors leading to the discontinuation of coverage. |
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By combining the incidence and termination datasets, insurers gain a comprehensive understanding of both the initiation and conclusion of LTC insurance policies. This complete perspective is vital for actuaries and analysts to assess risk, set appropriate reserves, design tailored products, and determine pricing strategies that reflect the true cost of providing LTC coverage. It also aids in regulatory compliance and ensures that insurance products are both financially viable for the provider and beneficial for the consumer. Through careful analysis of this aggregated data, insurers can predict trends, modify underwriting practices, and make informed decisions to manage their portfolios effectively. |
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### Dataset Sources |
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<!-- Provide the basic links for the dataset. --> |
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- **Repository:** SOA Actuarial Practice Modules[https://www.soa.org/sections/long-term-care/long-term-care-resources/] |
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## Uses |
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The long-term care (LTC) aggregated dataset is intended to serve as a foundational tool for actuaries and data scientists aiming to conduct comprehensive experience studies within the insurance sector. Experience studies are essential for understanding the past performance and projecting the future trends of insurance products, and this dataset provides the necessary detailed information to perform such analyses. |
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Traditional Framework(Used mostly in the insurance companies now): <br> |
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One of the primary uses of this dataset is to apply the chain ladder method, a traditional actuarial technique used to predict future claim costs and the reserve amounts needed to cover these potential claims. The chain ladder method relies on the assumption that historical claims development can predict future claims development, making it possible to estimate the reserves required for incurred but not reported (IBNR) claims as well as incurred but not enough reserved (IBNER) claims. |
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Bayesian Framework(Innovative methods recommended): <br> |
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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. |
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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. |
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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. |
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## Dataset Structure |
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### Incidence Dataset Structure Description |
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1. LTC Claim Incidence |
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- **Group_Indicator**: Categorical variable indicating the group to which the policyholder belongs. |
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- **Gender**: Categorical variable indicating the gender of the policyholder. |
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- **Issue_Age_Bucket**: Categorical variable denoting the age range when the policy was issued. |
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- **Incurred_Age_Bucket**: Categorical variable representing the age range when the claim was incurred. |
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- **Issue_Year_Bucket**: Categorical variable indicating the year range when the policy was issued. |
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- **Policy_Year**: Categorical variable indicating the specific year of the policy. |
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- **Marital_Status**: Categorical variable indicating the marital status of the policyholder. |
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- **Premium_Class**: Categorical variable indicating the classification of the premium. |
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- **Underwriting_Type**: Categorical variable indicating the type of underwriting applied to the policy. |
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- **Coverage_Type_Bucket**: Categorical variable indicating the coverage type category. |
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- **Tax_Qualification_Status**: Categorical variable indicating the tax qualification status of the policy. |
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- **Inflation_Rider**: Categorical variable indicating whether an inflation protection rider is attached to the policy. |
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- **Rate_Increase_Flag**: Categorical variable indicating if there has been a rate increase on the policy. |
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- **Restoration_of_Benefits**: Categorical variable indicating whether benefits have been restored. |
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- **NH_Orig_Daily_Ben_Bucket**: Categorical variable indicating the original daily benefit amount for nursing home care. |
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- **ALF_Orig_Daily_Ben_Bucket**: Categorical variable indicating the original daily benefit amount for assisted living facilities. |
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- **HHC_Orig_Daily_Ben_Bucket**: Categorical variable indicating the original daily benefit amount for home health care. |
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- **NH_Ben_Period_Bucket**: Categorical variable indicating the benefit period for nursing home care. |
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- **ALF_Ben_Period_Bucket**: Categorical variable indicating the benefit period for assisted living facilities. |
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- **HHC_Ben_Period_Bucket**: Categorical variable indicating the benefit period for home health care. |
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- **NH_EP_Bucket**: Categorical variable indicating the elimination period for nursing home care. |
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- **ALF_EP_Bucket**: Categorical variable indicating the elimination period for assisted living facilities. |
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- **HHC_EP_Bucket**: Categorical variable indicating the elimination period for home health care. |
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- **Region**: Categorical variable indicating the geographical region of the policy. |
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- **Active_Exposure**: Numeric variable indicating the active exposure amount. |
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- **Total_Exposure**: Numeric variable indicating the total exposure amount. |
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- **Claim_Count**: Numeric variable indicating the count of claims. |
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- **Count_NH**: Numeric variable indicating the count of nursing home claims. |
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- **Count_ALF**: Numeric variable indicating the count of assisted living facility claims. |
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- **Count_HHC**: Numeric variable indicating the count of home health care claims. |
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- **Count_Unk**: Numeric variable indicating the count of claims with unknown categorization. |
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2. LTC Claim Termination |
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### Termination Dataset Structure Description |
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- **Gender**: Categorical variable indicating the gender of the policyholder. |
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- **Incurred_Age_Bucket**: Categorical variable denoting the age range when the claim was incurred. |
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- **Incurred_Year_Bucket**: Categorical variable indicating the year range when the claim was incurred. |
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- **Claim_Type**: Categorical variable indicating the type of claim made. |
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- **Region**: Categorical variable indicating the geographical region of the policy. |
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- **Diagnosis_Category**: Categorical variable providing the category of diagnosis related to the claim. |
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- **Claim_Duration**: Categorical variable representing the duration that the claim has been active. |
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- **Exposure**: Numeric variable indicating the measure of risk that the insurer has been exposed to for the policy. |
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- **Deaths**: Numeric variable indicating the number of deaths among the policyholders. |
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- **Recovery**: Numeric variable indicating the number of policyholders who have recovered. |
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- **Terminations**: Numeric variable indicating the number of policy terminations. |
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- **Benefit_Expiry**: Numeric variable indicating the number of terminations due to benefits reaching their expiration date. |
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- **Others_Terminations**: Numeric variable indicating the number of terminations due to other unspecified reasons. |
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## Dataset Creation |
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### Curation Rationale |
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### Curation Rationale |
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The creation of this dataset stems from the increased popularity of long-term care (LTC) products amid rising longevity rates. It aims to provide insights into the trends and patterns of the claim filing process. The expansion of the LTC product line necessitates that insurance companies establish more accurate reserves to ensure financial health and sustainability. This dataset is pivotal in laying the groundwork for such analysis. |
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From my experience as a data analysis and administration intern at RGA, I understand the difficulties that arise from inadequate reserve practices and the lack of precise predictions in claim filings. These challenges underscore the need for a robust dataset that allows for comprehensive research into these critical areas. By leveraging this dataset, we can enhance the precision of actuarial predictions and reserve estimations, thereby contributing to the stability and efficiency of LTC insurance operations. |
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### Source Data |
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<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). --> |
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#### Data Collection and Processing |
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<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. --> |
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[More Information Needed] |
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#### Who are the source data producers? |
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<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. --> |
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[More Information Needed] |
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#### Personal and Sensitive Information |
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<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations. |
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## Dataset Card Authors [optional] |
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[Justin Kao] |
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## Dataset Card Contact |
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[justinkao.44@duke.edu] |
