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A text analytics-based decision support system for detecting fraudulent car insurance claims

A text analytics-based decision support system for detecting fraudulent car insurance claims
A text analytics-based decision support system for detecting fraudulent car insurance claims
Car insurance is a highly competitive business line in the insurance
industry. Companies face a very large number of claims year-on-year
and must decide carefully when to doubt a given claim, or when to simply cover the accident without question. In this presentation, we will show the results of a decision support system that ranks claims by their level of risk, suggesting which claims should be checked by an expert agent from the insurance agency. The decision support system analyses both common structured data, such as claim times and claimant information, and the report made by the claimant in free-text format. We show different strategies to deal with the text information, from using simple bag-of-words models, to much more complex embeddings using transforms such as Latent Semantic Analysis and fastText embeddings. Both sources of data, text-based and structured, are then used to create different predictive models estimating the probability that any given claim is fraudulent. Results indicate that there are clear gains in using more sophisticated models, but that data quality, specially having reliable fraud labels, might force the use of simpler models. We also will discuss how these models change business practices, by streamlining fraud detection as part of the day-to-day operation of insurance companies.
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Medina, Andrés
41b7d6b0-13ce-49d2-800b-fcb15bd01b2e
Joannon, Rodrigo
75564113-c8b8-4b4f-8ac9-0423b06a4bf3
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b
Bravo, Cristian
b22c4145-644e-40ee-85d8-431c59c3c71b
Medina, Andrés
41b7d6b0-13ce-49d2-800b-fcb15bd01b2e
Joannon, Rodrigo
75564113-c8b8-4b4f-8ac9-0423b06a4bf3
Weber, Richard
da9918d6-bc84-4c98-8ffe-2aaf7b58cf1b

Bravo, Cristian, Medina, Andrés, Joannon, Rodrigo and Weber, Richard (2018) A text analytics-based decision support system for detecting fraudulent car insurance claims. 29th European Conference on Operational Research, Valencia, Spain. 08 - 11 Jul 2018.

Record type: Conference or Workshop Item (Other)

Abstract

Car insurance is a highly competitive business line in the insurance
industry. Companies face a very large number of claims year-on-year
and must decide carefully when to doubt a given claim, or when to simply cover the accident without question. In this presentation, we will show the results of a decision support system that ranks claims by their level of risk, suggesting which claims should be checked by an expert agent from the insurance agency. The decision support system analyses both common structured data, such as claim times and claimant information, and the report made by the claimant in free-text format. We show different strategies to deal with the text information, from using simple bag-of-words models, to much more complex embeddings using transforms such as Latent Semantic Analysis and fastText embeddings. Both sources of data, text-based and structured, are then used to create different predictive models estimating the probability that any given claim is fraudulent. Results indicate that there are clear gains in using more sophisticated models, but that data quality, specially having reliable fraud labels, might force the use of simpler models. We also will discuss how these models change business practices, by streamlining fraud detection as part of the day-to-day operation of insurance companies.

Full text not available from this repository.

More information

Published date: 8 July 2018
Venue - Dates: 29th European Conference on Operational Research, Valencia, Spain, 2018-07-08 - 2018-07-11

Identifiers

Local EPrints ID: 422372
URI: https://eprints.soton.ac.uk/id/eprint/422372
PURE UUID: 4a3e3a44-c4d3-4de7-992d-609f8b36d38e
ORCID for Cristian Bravo: ORCID iD orcid.org/0000-0003-1579-1565

Catalogue record

Date deposited: 23 Jul 2018 16:30
Last modified: 14 Mar 2019 01:38

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Contributors

Author: Cristian Bravo ORCID iD
Author: Andrés Medina
Author: Rodrigo Joannon
Author: Richard Weber

University divisions

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