Dynamic ratemaking for insurance

• US 20020099596A1
• Filed: 11/26/2001
• Published: 07/25/2002
• Est. Priority Date: 11/27/2000
• Status: Abandoned Application

• Current Assignee: GMAC Insurance Company Online, Inc.
• Original Assignee: GMAC Insurance Company Online, Inc.
• Inventors: Geraghty, Michael Kevin
• Application Number:
  US09/992,408
• Publication Number:
  US 20020099596A1
• Time in Patent Office: Days
• US Class Current: 705/7.31
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities
• CPC Class Codes: G06Q 30/0204 Market segmentation
• CPC Class Codes: G06Q 30/0206 Price or cost determination based on market factors
• CPC Class Codes: G06Q 40/02 Banking, e.g. interest calculation or account maintenance credit or loans G06Q40/03

Abstract

A method of dynamically setting pricing rates for an insurance product includes observing the demand behavior of customers and assigning the customer to a pricing tier based at least in part on the observed demand behavior of the customer as compared to historical demand behavior of customers in a same market segment. Empirical data or statistical data of customer demand behavior is analyzed to adjust pricing tiers and market segments.

Claims

1. A method of dynamically determining a pricing rate for an insurance product, comprising:

(a) grouping demand for any of a plurality of products together into any of a plurality of market segments based on at least one of a plurality of pricing variables;

(b) analyzing demand behavior of each market segment for consumer price sensitivity;

(c) establishing a price associated with each analyzed market segment;

(d) generating forecasts of demand for each analyzed market segment; and

(e) optimizing a pricing rate of a specific product based on said generated forecasts of demand.

2. The method of claim 1, further comprising periodically repeating steps (a)-(e).

3. The method according to claim 1, wherein the pricing variables include rating variables and other behavior variables.

4. The method according to claim 1, wherein analyzing demand behavior includes generating a unique forecast of demand for each of the plurality of market segments, the unique forecast of demand based on historical demand consisting of a mean and higher moments of a demand surrogate for each of the plurality of market segments.

5. The method according to claim 4, further comprising testing the unique forecast of demand against actual observed demand.

6. The method according to claim 5, further comprising determining a difference between the unique forecast of demand and the actual observed demand, and if the difference is of statistical significance, identifying the market segment associated with the unique forecast of demand as a critical segment.

7. The method according to claim 6, wherein establishing a price associated with each analyzed market segment includes assigning each analyzed market segment to a price tier.

8. The method according to claim 7, wherein assigning each market segment to a price tier includes assigning said critical market segment to a candidate price tier, the candidate price tier associated with an increase in price compared to a previous price tier if the unique forecast of demand is larger than the actual observed demand and associated with a decrease in price compared to the previous price tier if the unique forecast of demand is smaller than the actual observed demand.

9. The method according to claim 1, wherein establishing a price associated with each analyzed market segment includes assigning each analyzed market segment to a price tier.

10. The method according to claim 1, wherein the generated forecasts of demand are used to predict a number of conversions of a price tier assigned to each of the analyzed markets.

11. The method according to claim 1, wherein optimizing a pricing rate includes:

determining a percentage change in consumer demand that is expected to occur in response to a percentage change in a rate of a price tier containing said analyzed market segment; and

implementing a change in the rate of a price tier based upon said determined percentage change in consumer demand such that a profit from said pricing tier is maximized.

14. The method of claim 13, further comprising:

compiling the attributes of a plurality of customers; and

adjusting rates associated with the plurality of price tiers based on the compiled attributes.

15. The method of claim 13, wherein price tier assignments are generated based on a price tier assignment database.

16. The method of claim 13, wherein the customer is assigned to a price tier based on the attributes and on a plurality of customer characteristics.

17. The method of claim 13, wherein generating of an optimized price is based on forecasted acceptance rates and assigning a price tier that implements the optimized price.

19. The method of claim 18, further comprising:

including data associated with the particular customer in the data associated with the historical behavior of a plurality of customers for the product to provide new compiled data; and

adjusting the market segments based on the new compiled data.

12. A method of dynamically determining a pricing rate for an insurance product, comprising:

grouping demand for any of a plurality of products together into any of a plurality of market segments based on a group of pricing variables, wherein the pricing variables include rating variables and other behavior variables;

assigning each market segment to a price tier;

generating a unique forecast of demand for each market segment, including testing the unique forecast of demand against actual observed demand, and determining a difference between the unique forecast of demand and the actual observed demand, and if the difference is of statistical significance, identifying the market segment associated with the unique forecast of demand as a critical segment, wherein the unique forecast of demand is based on historical demand consisting of a mean and higher moments of a demand surrogate for each of the plurality of market segments; and

adjusting the price tier of the market segment according to the difference, if the difference is of statistical significance.

13. A method of determining an optimized price for offering an insurance product to a customer, comprising:

analyzing attributes of a customer'"'"'s demand behavior;

assigning the customer to one of a plurality of price tiers based upon the attributes of the customer'"'"'s demand behavior;

forecasting whether the customer will accept an offer to purchase the product based on the assigned price tier; and

generating an optimized price based on the forecast.

18. A method of dynamically setting pricing rates for an insurance product comprising:

compiling data associated with historical demand behavior of a plurality of customers for the product;

developing a plurality of market segments based on the data associated with historical demand behavior, wherein each market segment has common attributes;

analyzing attributes of a particular customer and assigning the customer to a market segment having common attributes;

evaluating customer characteristics of the particular customer;

assigning the customer to a pricing tier based on the assigned market segment and the customer characteristics; and

providing a pricing rate for the particular customer based on the pricing tier.