Strategies for detecting fraudulent claims in the automobile insurance industry

Viaene, Stijn; Ayuso, Mercedes; Guillén, Montserrat; Gheel, Dirk Van; Dedene, Guido

doi:10.1016/j.ejor.2005.08.005

Cited by 77 publications

(57 citation statements)

References 18 publications

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“…In 2007, Pinquet et al [25] and Viaene et al [26] both studied logistic regression with insurance fraud, concentrating on a database of Spanish automobile insurance claims. Bhattacharyya et al [27] performed several credit card fraud experiments, comparing two common classification solutions against the well-known logistic regression and observing results across various common metrics.…”

Section: Logistic Regressionmentioning

confidence: 99%

A state of the art survey of data mining-based fraud detection and credit scoring

Zhou¹,

Cheng²,

Zhu³

et al. 2018

MATEC Web Conf.

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Abstract.Credit risk has been a widespread and deep penetrating problem for centuries, but not until various credit derivatives and products were developed and novel technologies began radically changing the human society, have fraud detection, credit scoring and other risk management systems become so important not only to some specific firms, but to industries and governments worldwide. Frauds and unpredictable defaults cost billions of dollars each year, thus, forcing financial institutions to continuously improve their systems for loss reduction. In the past twenty years, amounts of studies have proposed the use of data mining techniques to detect frauds, score credits and manage risks, but issues such as data selection, algorithm design, and hyperparameter optimization affect the perceived ability of the proposed solutions and it is difficult for auditors and researchers to explore and figure out the highest level of general development in this area. In this survey we focus on a state of the art survey of recently developed data mining techniques for fraud detection and credit scoring. Several outstanding experiments are recorded and highlighted, and the corresponding techniques, which are mostly based on supervised learning algorithms, unsupervised learning algorithms, semisupervised algorithms, ensemble learning, transfer learning, or some hybrid ideas are explained and analysed. The goal of this paper is to provide a dense review of up-to-date techniques for fraud detection and credit scoring, a general analysis on the results achieved and upcoming challenges for further researches.

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Section: Logistic Regressionmentioning

confidence: 99%

A state of the art survey of data mining-based fraud detection and credit scoring

Zhou¹,

Cheng²,

Zhu³

et al. 2018

MATEC Web Conf.

View full text Add to dashboard Cite

show abstract

“…Hence it is not always possible to find special fraud investigation units in companies, many of which often include anti-fraud measures as part of the procedures to be carried out by appraisers, without there being specific acknowledgment of the task. Source: Viaene et al (2007) Quantifying operational risk from the point of view of external fraud has to take into account whether or not the company carries out claim auditing for fraud detection purposes. If the company does not carry out a thorough fraud investigation, operational risk can be quantified by taking into account the expected proportion of fraudulent claims and the total compensation paid by the company for this concept.…”

Section: External Fraud: Policyholder Fraudmentioning

confidence: 99%

Loss Risk Through Fraud in Car Insurance

Ayuso

Bolancé

Guillén³

2011

SSRN Journal

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Our objective is to analyse fraud as an operational risk for the insurance company. We study the effect of a fraud detection policy on the insurer's results account, quantifying the loss risk from the perspective of claims auditing. From the point of view of operational risk, the study aims to analyse the effect of failing to detect fraudulent claims after investigation. We have chosen VAR as the risk measure with a non-parametric estimation of the loss risk involved in the detection or non-detection of fraudulent claims. The most relevant conclusion is that auditing claims reduces loss risk in the insurance company.

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“…Viaene et al (2002) actually apply different techniques in their work, from logistic regression, k-nearest neighbor, decision trees and Bayesian neural network to support vector machine, naive Bayes and tree-augmented naive Bayes. Also in Viaene et al (2007), logistic regression is applied.…”

Section: Fraud Detection/prevention Literature Reviewmentioning

confidence: 99%

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

Jans¹,

Lybaert²,

Vanhoof³

2009

IJDAR

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Abstract. Fraud is a million dollar business and it is increasing every year. Both internal and external fraud present a substantial cost to our economy worldwide. A review of the academic literature learns that the academic community only addresses external fraud and how to detect this type of fraud. Little or no effort to our knowledge has been put in investigating how to prevent ánd to detect internal fraud, which we call 'internal fraud risk reduction'. Taking together the urge for research in internal fraud and the lack of it in academic literature, research to reduce internal fraud risk is pivotal. Only after having a framework in which to implement empirical research, this topic can further be investigated. In this paper we present the IFR² framework, deduced from both the academic literature and from current business practices, where the core of this framework suggests to use a data mining approach.

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Strategies for detecting fraudulent claims in the automobile insurance industry

Cited by 77 publications

References 18 publications

A state of the art survey of data mining-based fraud detection and credit scoring

A state of the art survey of data mining-based fraud detection and credit scoring

Loss Risk Through Fraud in Car Insurance

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

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