# data[].attributes.body
How accurate is the Expected Death Claims in the last four years?

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- Determine Actual vs Expected claims ratio per gender, age bracket, member type, pooling arrangement.

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Is the mortality rate used still accurate in computing the expected death claims?

# data[].attributes.body
-Identify which correct mortality table from Society of Actuaries should be used.

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Which member demographic has accurate estimated death claims?

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-Determine the key factors that affect mortality rate for GLAPI clients based on historical data.

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How do we accurately compute for the expected death claims per member demographic?

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-Identify forecasting method to accurately compute for expected death claims per Gender based on historical data of GLAPI

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-What are the usual causes of death per member demographic?

# data[].attributes.body
-Identify forecasting method to accurately compute for expected death claims per Age Group based on historical data of GLAPI

# data[].attributes.body
- Compare estimated claims vs actual claims by Gender

# data[].attributes.body
- Compare estimated claims vs actual claims by Age Group

# data[].attributes.body
- Compare estimated claims vs actual claims by Sales Distribution Channel

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- Compare estimated claims vs actual claims by Pooling Arrangement

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- Compare estimated claims vs actual claims by Member Type (Employee/Dependent)

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How accurate is the estimated claims amount per account?

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- Determine the acceptable level of Actual vs Expected claims ratio per account

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- Identify patterns in Actual vs Expected Claims and Amount per month and year per account

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How can we ensure that estimated claims is accurate?

# data[].attributes.body
-Identify forecasting method to accurately compute for expected death claims per Sales Distribution Channel based on historical data of GLAPI

# data[].attributes.body
-Identify forecasting method to accurately compute for expected death claims per Pooling Arrangement based on historical data of GLAPI

# data[].attributes.body
-Identify forecasting method to accurately compute for expected death claims per Member Type (Employee/Dependent) based on historical data of GLAPI

# data[].attributes.body
-Compute for the Exposure count per cause of death

# data[].attributes.body
-Determine if there is a relationship between the cause of death and age and/or gender.

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- Identify patterns and trends on top 10 accounts, industries, illnesses, providers with higher actual claims paid than estimated claims

# data[].attributes.body
-Identify forecasting method to accurately compute for estimated claims based on historical data per account

# data[].attributes.body
'- Identify the Top Illnesses/Top Areas/Top Industries/Top Providers with higher Actual Claims vs Estimated Claims by Amount.

# data[].attributes.body
-Identify the Top Illnesses/Top Areas/Top Industries/Top Providers and compute for the average claims amount per age group every month.

# data[].attributes.body
-Identify the Top Illnesses/Top Areas/Top Industries/Top Providers that are consistently above the acceptable level for medical loss ratio every year.

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-Analyze the trend in claims amount per Illness/area/industry/providers per Month.

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Is the current premium pricing per account still profitable?

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-Determine the difference between Actual Claims and Premium Price for last year vs this year

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-Compare Estimated Incurred Claims vs Actual Claims Paid of each account for last year vs this year

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How can we ensure that each account has correct premium pricing that is profitable?

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How did the pandemic affect our health and death claims?

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-Establish the baseline for health and death claims pre-pandemic.

# data[].attributes.body
-Compare the baseline vs health and death claims during the pandemic.