MétaCan
Menu
Back to cohort
Record W306439780

Tackling Insurance Fraud: Law and Practice

2007· article· en· W306439780 on OpenAlexaboutno aff
Richard A. Derrig

Bibliographic record

VenueRisk Management and Insurance Review · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLaw, logistics, and international trade
Canadian institutionsnot available
Fundersnot available
KeywordsInsurance lawReinsuranceLawInsurance fraudInsurance policyGeneral insuranceSuspectBad faithBusinessActuarial sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Tackling Insurance Fraud: Law and Practice, by Dexter Morse and Lynne Skajaa, Informa Professional, London, UK. This volume bills itself as one of a series of Practical Insurance Guides, along with others such as Insurance Disputes, Good Faith and Insurance, and Marine Insurance all with an emphasis on law. Informa Professional is the publisher of a London insurance daily Insurance Day and a purveyor of conferences (Reinsurance Summit 2006), training courses (none listed yet), and distance learning (masters degrees in various law fields, such as EU law, food law and employment law, and with various universities) as shown on their Website (www.informalaw.com). Informa's interests appear to be principally legal (247 products, 27 in insurance and banking, and 73 in maritime law). This volume fits into the insurance and banking law category. The authors are both accomplished lawyers in the UK, Dexter Morse with a reinsurer and Lynne Skajaa with several law firms dealing with maritime issues and with the University of Southampton. As you would suspect the book deals with insurance fraud with a decidedly legalistic point of view. Although the fraud coverage is primarily from a UK perspective, the authors present some universal concepts such as What is insurance fraud? and Who is the typical fraudster? and contrast the UK experience with the United States, Canada, Australia, New Zealand, South Africa, and European efforts in some 34 of the book's 158 pages. You will not find theoretical models of behavior, with underlying subjective probabilistic models of outcomes favorable to the fraudsters, but you will find a discussion of both the problem of insurance fraud and details about the means of detection, deterrence, and prosecution, all in a readable and well sequenced set of chapters. Chapter 1 begins by exposing a worldwide problem for combating insurance fraud found also in the UK, namely, the absence of specific statutes that deal explicitly with insurance fraud. The authors cite relevant sections of the Theft Act of 1968, and subsequent case law that cover deception and dishonesty in obtaining property (claim settlement), in gaining a pecuniary advantage (policy coverage) and in falsifying accounts (large scale fraud). The Theft Act, updated in 1996 to include banking but not insurance transactions, provides the analogs to the use in the United States of larceny and mail fraud statutes to prosecute insurance fraud prior to recent state-by-state adoption of laws, some by line of insurance, specifying penalties for insurance fraud per se. case law and the Perjury Act of 1911 are also cited for their usefulness in rounding out the legal environment in the UK. Typical fraudsters are categorized in Chapter 2 into three categories: The average insured succumbing to what we call opportunistic the full or part-time criminal, and organized gangs, all motivated by greed, financial difficulty, or a sense of recovering premiums not paid out in claims. There does not seem to be a category that covers professional providers such as doctors and lawyers who facilitate fraud in the guise of legitimate claims2 or a category for those who create phony insurers or loot existing companies of their assets. Thus the focus of the book is the retail aspect of application and claim fraud. Chapter 3 reviews so-called fraud indicators, those yes-no objective and subjective statements about the circumstances surrounding the filing of a claim such as 'The insured is very knowledgeable about insurance terms and claims procedures. Such indicators may be specific to line of business, especially property or motor lines.3 Chapter 4 confronts the problem of separating criminal fraud from civil fraud, the latter being unethical practices, at least in someone's eyes, known colloquially as soft fraud. The authors properly view the distinction in terms of contract law and resolution of contract disputes. Chapters 5 and 6 apply the general legal analysis to their specific interests in marine and reinsurance lines of insurance. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0030.010
Scholarly communication0.0170.017
Open science0.0010.007
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0100.008

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.279
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2007
Admission routes1
Has abstractyes

Explore more

Same venueRisk Management and Insurance ReviewSame topicLaw, logistics, and international tradeFrench-language works237,207