A Relational Data Matching Model for Enhancing Individual Loss Experience: An Example from Crop Insurance

North American Actuarial Journal, 23(4), 551-572.

44 Pages Posted: 28 May 2016 Last revised: 21 May 2020

See all articles by Lysa Porth

Lysa Porth

University of Manitoba - Warren Centre for Actuarial Studies and Research; University of Waterloo - Department of Statistics and Actuarial Science; University of Manitoba - Department of Agribusiness and Agricultural Economics

Ken Seng Tan

University of Waterloo

Wenjun Zhu

Nanyang Business School, Nanyang Technological University

Date Written: July 24, 2019

Abstract

The focus of this paper is on predictive analytics regarding data scarcity and credibility, which are major difficulties facing the agricultural insurance sector often due to limited loss experience data for those infrequent, but, extreme weather events. A new relational data-matching model is presented to predict individual farmer yields in the absence of farm-level data. The relational model defines a similarity measure based on an Euclidean distance metric that considers weather information, farm size,county size and the coefficient of variation of yield, to search for the most "similar" region in a different country to borrow individual loss experience data that is otherwise not available. Detailed farm-level and county-level corn yield data in Canada and the U.S. are used to empirically evaluate the proposed relational model. Compared to the benchmark model, the relational model developed in this paper has lower prediction error with smaller variation, and is also able to recover the actual premium rate more accurately. While the example in this paper uses historical data from Canada and the U.S. to illustrate the approach, the model may be extended to other countries, including developing countries, where farm-level data may not be available. This provides a new approach for insurers, reinsurers, and governments to enhance risk modelling,
pricing, and the development of new insurance programs.

Keywords: Relational Model, Aggregation Bias, Shortness of Data, Euclidean Distance, Crop Insurance, Yield Forecasting, Ratemaking

JEL Classification: C13, C15, C16, G22, Q19

Suggested Citation

Porth, Lysa and Tan, Ken Seng and Zhu, Wenjun, A Relational Data Matching Model for Enhancing Individual Loss Experience: An Example from Crop Insurance (July 24, 2019). North American Actuarial Journal, 23(4), 551-572., Available at SSRN: https://ssrn.com/abstract=2784735 or http://dx.doi.org/10.2139/ssrn.2784735

Lysa Porth

University of Manitoba - Warren Centre for Actuarial Studies and Research ( email )

638 Drake Centre, 181 Freedman Crescent
Winnipeg, MB R3T 2N2
Canada

University of Waterloo - Department of Statistics and Actuarial Science ( email )

Waterloo, Ontario N2L 3G1
Canada

University of Manitoba - Department of Agribusiness and Agricultural Economics ( email )

Winnipeg, MB, R3T 2N2
Canada

Ken Seng Tan

University of Waterloo ( email )

Waterloo, Ontario N2L 3G1
Canada

Wenjun Zhu (Contact Author)

Nanyang Business School, Nanyang Technological University ( email )

50 Nanyang Avenue
Singapore, 639798
Singapore
(65) 6592-1859 (Phone)

HOME PAGE: http://sites.google.com/view/wenjun-zhu

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