A data set for modeling claims processes—TSA claims data

• Date: 01 September 2020
• Author: Mary Kelly,Zilin Wang
• Published date: 01 September 2020
• DOI: http://doi.org/10.1111/rmir.12155

Risk Manag Insur Rev. 2020;23:269–276. wileyonlinelibrary.com/journal/rmir

|

269

Received: 12 June 2020

|

Accepted: 4 August 2020

DOI: 10.1111/rmir.12155

DATA INSIGHT

A data set for modeling claims processes—TSA

claims data

Mary Kelly

1

|Zilin Wang

2

1

Department of Business, Lazaridis School

of Business & Economics, Wilfrid Laurier

University, Waterloo, Ontario, Canada

2

Department of Mathematics, Faculty of

Science, Wilfrid Laurier University,

Waterloo, Ontario, Canada

Correspondence

Mary Kelly, Department of Business,

Lazaridis School of Business & Economics,

Wilfrid Laurier University, Waterloo, ON,

N2L 3C5 Canada.

Email: mkelly@wlu.ca

Abstract

This data insight highlights the Transportation

Security Administration (TSA) claims data as an

underused data set that would be particularly useful

to researchers developing statistical models to ana-

lyze claim frequency and severity. Individuals who

have been injured or had items damaged, lost or

stolen may make a claim for losses to the TSA. The

federal government reports information on every

claim from 2002 to 2017 at https://www.dhs.gov/tsa-

claims-data. Information collected includes claim

date and type and site as well as closed claim amount

and disposition (whether it was approved in full,

denied, or settled. We provide summary statistics on

the frequency and the severity of the data for the

years 2003 to 2015. The data set has several unique

features including severity is not truncated (there is

no deductible), there are significant mass points in

the severity data, and the frequency data shows a

high degree of auto correlation if compiled on a

weekly basis, and substantial frequency mass points

at zero for daily data.

KEYWORDS

claims data, claims modeling, frequency and severity models

----------------------------------------------------------------------------------------------------

© 2020 The American Risk and Insurance Association