Relevant bibliographies by topics / Detection of insurance fraud

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Detection of insurance fraud'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 February 2022

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Journal articles on the topic "Detection of insurance fraud"

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Munavalli, Sahana, and Sanjeevakumar M. Hatture. "Fraud Detection in Healthcare System using Symbolic Data Analysis." International Journal of Innovative Technology and Exploring Engineering 10, no. 9 (July 30, 2021): 1–7. http://dx.doi.org/10.35940/ijitee.h9269.0710921.

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In the era of digitization the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or distortion for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the anomaly detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have been set apart by data mining programming. One of the issues is the abuse of the medical insurance systems. Manual detection of frauds in the healthcare industry is strenuous work. Fraud and Abuse in the Health care system have become a significant concern and that too inside health insurance organizations, from the most recent couple of years because of the expanding misfortunes in incomes, handling medical claims have become a debilitating manual assignment, which is done by a couple of clinical specialists who have the duty of endorsing, adjusting, or dismissing the appropriations mentioned inside a restricted period from their gathering. Standard data mining techniques at this point do not sufficiently address the intricacy of the world. In this way, utilizing Symbolic Data Analysis is another sort of data analysis that permits us to address the intricacy of the real world and to recognize misrepresentation in the dataset.
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Schiller, Jorg. "The Impact of Insurance Fraud Detection Systems." Journal of Risk Insurance 73, no. 3 (September 2006): 421–38. http://dx.doi.org/10.1111/j.1539-6975.2006.00182.x.

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Gomes, Chamal, Zhuo Jin, and Hailiang Yang. "Insurance fraud detection with unsupervised deep learning." Journal of Risk and Insurance 88, no. 3 (July 26, 2021): 591–624. http://dx.doi.org/10.1111/jori.12359.

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IKUOMOLA, A. J., and O. E. Ojo. "AN EFFECTIVE HEALTH CARE INSURANCE FRAUD AND ABUSE DETECTION SYSTEM." Journal of Natural Sciences Engineering and Technology 15, no. 2 (November 22, 2017): 1–12. http://dx.doi.org/10.51406/jnset.v15i2.1662.

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Due to the complexity of the processes within healthcare insurance systems and the large number of participants involved, it is very difficult to supervise the systems for fraud. The healthcare service providers’ fraud and abuse has become a serious problem. The practices such as billing for services that were never rendered, performing unnecessary medical services and misrepresenting non-covered treatment as covered treatments etc. not only contribute to the problem of rising health care expenditure but also affect the health of the patients. Traditional methods of detecting health care fraud and abuse are time-consuming and inefficient. In this paper, the health care insurance fraud and abuse detection system (HECIFADES) was proposed. The HECIFADES consist of six modules namely: claim, augment claim, claim database, profile database, profile updater and updated profiles. The system was implemented using Visual Studio 2010 and SQL. After testing, it was observed that HECIFADES was indeed an effective system for detecting fraudulent activities and yet very secured way for generating medical claims. It also improves the quality and mitigates potential payment risks and program vulnerabilities.
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Flynn, Kathryn. "Financial fraud in the private health insurance sector in Australia." Journal of Financial Crime 23, no. 1 (December 31, 2015): 143–58. http://dx.doi.org/10.1108/jfc-06-2014-0032.

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Purpose – The purpose of this article is to explore financial fraud in the private health insurance sector in Australia. Fraud in this sector has commonalities to other countries with similar health systems but in Australia it has garnered some unique characteristics. This article sheds light on these features, especially the fraught relationship between the private health funds and the public health insurance agency, Medicare and the problematic impact of the Privacy Act on fraud detection and financial recovery. Design/methodology/approach – A qualitative methodological approach was used, and interviews were conducted with fraud managers from Australia’s largest private health insurance funds and experts in fields connected to health fraud detection. Findings – All funds reported a need for more technological resources and higher staffing levels to manage fraud. Inadequate resourcing has the predictable outcome of a low detection and recovery rate. The fund managers had differing approaches to recovery action and this ranged from police action, the use of debt recovery agencies, to derecognition from the health fund. As for present and future harm to the industry, the funds found on-line claiming platforms a major threat to the integrity of their insurance system. In addition, they all viewed the Privacy Act as an impediment to managing fraud against their organizations and they desired that there be greater information sharing between themselves and Medicare. Originality/value – This paper contributes to the knowledge of financial fraud in the private health insurance sector in Australia.
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Sowah, Robert A., Marcellinus Kuuboore, Abdul Ofoli, Samuel Kwofie, Louis Asiedu, Koudjo M. Koumadi, and Kwaku O. Apeadu. "Decision Support System (DSS) for Fraud Detection in Health Insurance Claims Using Genetic Support Vector Machines (GSVMs)." Journal of Engineering 2019 (September 2, 2019): 1–19. http://dx.doi.org/10.1155/2019/1432597.

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Fraud in health insurance claims has become a significant problem whose rampant growth has deeply affected the global delivery of health services. In addition to financial losses incurred, patients who genuinely need medical care suffer because service providers are not paid on time as a result of delays in the manual vetting of their claims and are therefore unwilling to continue offering their services. Health insurance claims fraud is committed through service providers, insurance subscribers, and insurance companies. The need for the development of a decision support system (DSS) for accurate, automated claim processing to offset the attendant challenges faced by the National Health Insurance Scheme cannot be overstated. This paper utilized the National Health Insurance Scheme claims dataset obtained from hospitals in Ghana for detecting health insurance fraud and other anomalies. Genetic support vector machines (GSVMs), a novel hybridized data mining and statistical machine learning tool, which provide a set of sophisticated algorithms for the automatic detection of fraudulent claims in these health insurance databases are used. The experimental results have proven that the GSVM possessed better detection and classification performance when applied using SVM kernel classifiers. Three GSVM classifiers were evaluated and their results compared. Experimental results show a significant reduction in computational time on claims processing while increasing classification accuracy via the various SVM classifiers (linear (80.67%), polynomial (81.22%), and radial basis function (RBF) kernel (87.91%).
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Belhadji, El Bachir, George Dionne, and Faouzi Tarkhani. "A Model for the Detection of Insurance Fraud." Geneva Papers on Risk and Insurance - Issues and Practice 25, no. 4 (October 2000): 517–38. http://dx.doi.org/10.1111/1468-0440.00080.

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Kim, Yongbum, and Miklos A. Vasarhelyi. "A Model to Detect Potentially Fraudulent/Abnormal Wires of an Insurance Company: An Unsupervised Rule-Based Approach." Journal of Emerging Technologies in Accounting 9, no. 1 (December 1, 2012): 95–110. http://dx.doi.org/10.2308/jeta-50411.

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ABSTRACT Fraud prevention/detection is an important function of internal control. Prior literature focused mainly on fraud committed by external parties, such as customers. However, according to a survey by the Association of Certified Fraud Examiners (ACFE 2009), it was noted that employees posed the greatest fraud threat. This study proposes profiling fraud using an unsupervised learning method. The fraud detection model is based on potential fraud/anomaly indicators in the wire transfer payment process of a major insurance company in the United States. Each indicator is assigned an arbitrary score based on its severity. Once an aggregate score is calculated, those wire transfer payments whose total scores are above a threshold will be suggested for investigation. Our contribution is to report what we have learned and to document our findings using fraud/anomaly indicators to detect potential fraud and/or errors on real data from a major insurance company.
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Lægreid, I. "Automatic Fraud Detection — Does it Work?" Annals of Actuarial Science 2, no. 2 (September 2007): 271–88. http://dx.doi.org/10.1017/s1748499500000361.

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ABSTRACTThe aim of automatic fraud detection is to assist claims handlers by selecting claims which are potentially fraudulent. Such methods must be based on information routinely available. The present paper makes use of logistic regression and the validity of these models as demonstrated on fresh data from household and motor insurance. A second contribution is a strategy for selecting claims that should receive further attention.
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Santoso, Budi, Julita Hendrartini, Bambang Udji Djoko Rianto, and Laksono Trisnantoro. "SYSTEM FOR DETECTION OF NATIONAL HEALTHCARE INSURANCE FRAUD BASED ON COMPUTER APPLICATION." Public Health of Indonesia 4, no. 2 (June 21, 2018): 46–56. http://dx.doi.org/10.36685/phi.v4i2.199.

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Background: The national healthcare insurance (JKN) has been in deficit since 2014-2016; one of the causes is fraud inpatient hospital service. Objective: This study aimed to analyze the validity, reliability and effectiveness of detection system of national healthcare insurance fraud based on computer application in hospital.Methods: Cross-sectional method was used. Fraud data were collected at one episode in the inpatient JKN participant service.Results: Validity was assessed by Fischer exact test. The interpretation was done by hospital internal verification officer and BPJS Kesehatan verification officer. There were only 2 out of 1.106 services claims were different, resulted in p-value 0.01. Reliability was assessed using Human Organization Technology Benefit questionnaire filled by admission administrator officer, BPJS Kesehatan officer and hospital internal verification officer; and then analyzed using Stata® software resulting in Cronbach’s alpha value of 0.8. Effectiveness was assessed by reducing potential fraud, conducted by RSUP dr. Soeradji Tirtonegoro from May until July 2017, which on May 2018 there were 8 findings, June 1 finding, and on July 2018 had no finding.Conclusion: System for detection of national healthcare insurance fraud based on computer application is valid, reliable and effective to be implemented in inpatient service in hospital.
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Dissertations / Theses on the topic "Detection of insurance fraud"

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Roberts, Terisa. "The use of credit scorecard design, predictive modelling and text mining to detect fraud in the insurance industry / Terisa Roberts." Thesis, North-West University, 2011. http://hdl.handle.net/10394/10347.

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The use of analytical techniques for fraud detection and the design of fraud detection systems have been topics of several research projects in the past and have seen varying degrees of success in their practical implementation. In particular, several authors regard the use of credit risk scorecards for fraud detection as a useful analytical detection tool. However, research on analytical fraud detection for the South African insurance industry is limited. Furthermore, real world restrictions like the availability and quality of data elements, highly unbalanced datasets, interpretability challenges with complex analytical techniques and the evolving nature of insurance fraud contribute to the on-going challenge of detecting fraud successfully. Insurance organisations face financial instability from a global recession, tighter regulatory requirements and consolidation of the industry, which implore the need for a practical and effective fraud strategy. Given the volumes of structured and unstructured data available in data warehouses of insurance organisations, it would be sensible for an effective fraud strategy to take into account data-driven methods and incorporate analytical techniques into an overall fraud risk assessment system. Having said that, the complexity of the analytical techniques, coupled with the effort required to prepare the data to support it, should be carefully considered as some studies found that less complex algorithms produce equal or better results. Furthermore, an over reliance on analytical models can underestimate the underlying risk, as observed with credit risk at financial institutions during the financial crisis. An attractive property of the structure of the probabilistic weights-of-evidence (WOE) formulation for risk scorecard construction is its ability to handle data issues like missing values, outliers and rare cases. It is also transparent and flexible in allowing the re-adjustment of the bins based on expert knowledge or other business considerations. The approach proposed in the study is to construct fraud risk scorecards at entity level that incorporate sets of intrinsic and relational risk factors to support a robust fraud risk assessment. The study investigates the application of an integrated Suspicious Activity Assessment System (SAAS) empirically using real-world South African insurance data. The first case study uses a data sample of short-term insurance claims data and the second a data sample of life insurance claims data. Both case studies show promising results. The contributions of the study are summarised as follows: The study identified several challenges with the use of an analytical approach to fraud detection within the context of the South African insurance industry. The study proposes the development of fraud risk scorecards based on WOE measures for diagnostic fraud detection, within the context of the South African insurance industry, and the consideration of alternative algorithms to determine split points. To improve the discriminatory performance of the fraud risk scorecards, the study evaluated the use of analytical techniques, such as text mining, to identify risk factors. In order to identify risk factors from large sets of data, the study suggests the careful consideration of both the types of information as well as the types of statistical techniques in a fraud detection system. The types of information refer to the categories of input data available for analysis, translated into risk factors, and the types of statistical techniques refer to the constraints and assumptions of the underlying statistical techniques. In addition, the study advocates the use of an entity-focused approach to fraud detection, given that fraudulent activity typically occurs at an entity or group of entities level.
PhD, Operational Research, North-West University, Vaal Triangle Campus, 2011
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Hradilová, Zuzana. "Pojistné podvody." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2014. http://www.nusl.cz/ntk/nusl-232859.

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This diploma thesis deals with problems of insurance fraud in the Czech Republic. The thesis is decided into the several separate parts. The teoretical part describes characteristics of insuracne fraud itselfs, its classification, profile of fraud perpetor and reason of committing instance fraud at all. The next part describes detection of insurance fraud and the subsecvent procedure in investigating insurance fraud. The goal of practical part of diploma thesis is analysis of insurance fraud and questionnaire survey. There will be describe the prevetion of insurance fraud and in the end, there will be several specific cases of insurance fraud.
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Pražanová, Markéta. "Problematika pojistného podvodu v ČR." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-75473.

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The insurance fraud is frequent type of criminality at the present time. The perpetrators of this crime cause heavy economic damages to insurance companies. Objective of the thesis called "The insurance fraud in the Czech Republic" is to evaluate the current state of the problem of insurance fraud in the Czech Republic from the perspective of insurance companies, law enforcement authorities and new legislation. As well to describe the way of detection and investigation, characterize the offender, analyze the most frequent cases, typical methods of committing insurance fraud and to evaluate the statistics and trends from previous years. In the thesis are explained the principles of detecting insurance fraud in insurance companies and the preventive measures. Part of the thesis is to identify weaknesses in the fight against the insurance fraud.
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Konopíková, Marie. "Pojistné podvody." Master's thesis, Vysoká škola ekonomická v Praze, 2014. http://www.nusl.cz/ntk/nusl-205812.

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This thesis is focused on theme of insurance´s fraud, primarily from the legal aspects. The thesis consist of legislative of insurance fraud according to the Criminal Code, also including a list of punishment. The following part dedicate to active insurers fight against cheats, their investigation and using more effective instruments and measures of their prevention. The thesis doesn´t forget statistical data and development in detection of insurance fraud in last 5 years. There is also the judicature of High Court and the examples of practise.
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Gažová, Iva. "Pojistné podvody." Master's thesis, Vysoké učení technické v Brně. Ústav soudního inženýrství, 2010. http://www.nusl.cz/ntk/nusl-232511.

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This diploma thesis deals with the problems of insurance fraud in our society. The thesis is divided into several relatively separate sections. The theoretical part describes a basic characteristic, classification and origins of insurance fraud and it deals with a general description of perpetrators of fraudulent actions. An analysis of fraudulent actions in life and non-life insurance is carried out in the theoretical part of the diploma thesis. This work characterises the importance and the mutual relationship between detection and investigation of fraudulent actions. It also highlights the facts which aid and abet insurance fraud. The aim of the practical part of the diploma thesis was to carry out an analysis of various insurance fraud cases in the realm of motor insurance according to the subject, object and the most frequent variants of fraudulent actions and consequently create a profile of the perpetrator of insurance fraud on the basis of the evaluation of the analysis. The practical case study of client’s expedient behaviour enables us to look on detection of the particular insurance fraud. The end of the diploma thesis deals with recommendations for the measures which should be taken to fight insurance fraud.
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da, Rosa Raquel C. "An Evaluation of Unsupervised Machine Learning Algorithms for Detecting Fraud and Abuse in the U.S. Medicare Insurance Program." Thesis, Florida Atlantic University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10815097.

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The population of people ages 65 and older has increased since the 1960s and current estimates indicate it will double by 2060. Medicare is a federal health insurance program for people 65 or older in the United States. Medicare claims fraud and abuse is an ongoing issue that wastes a large amount of money every year resulting in higher health care costs and taxes for everyone. In this study, an empirical evaluation of several unsupervised machine learning approaches is performed which indicates reasonable fraud detection results. We employ two unsupervised machine learning algorithms, Isolation Forest, and Unsupervised Random Forest, which have not been previously used for the detection of fraud and abuse on Medicare data. Additionally, we implement three other machine learning methods previously applied on Medicare data which include: Local Outlier Factor, Autoencoder, and k-Nearest Neighbor. For our dataset, we combine the 2012 to 2015 Medicare provider utilization and payment data and add fraud labels from the List of Excluded Individuals/Entities (LEIE) database. Results show that Local Outlier Factor is the best model to use for Medicare fraud detection.

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Minár, Tomáš. "Detekce pojistných podvodů." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2012. http://www.nusl.cz/ntk/nusl-223691.

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This thesis focuses on the area of detection of potential insurance frauds by using Business Intelligence (BI) and its practical application to real data of compulsory and accident insurance. It describes the basic concepts of insurance business, the individual layers of BI architecture, and a detailed description of the implementation process from data transformation through the use of advanced analytical methods to the presentation of acquired information.
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Gill, Karen Ann. "Insurance fraud : causes, characteristics and prevention." Thesis, University of Leicester, 2014. http://hdl.handle.net/2381/29106.

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Although there is a growing volume of research on various kinds of fraud, relatively little has been written about insurance fraud. Even fewer studies have been undertaken on the prevention of insurance fraud. This study aims to fill this gap. It focuses not on large-scale corporate fraud but on individuals ‘fiddling’ their home, motor and travel policies. During the course of this study, the researcher surveyed the public and found that insurance fraud is commonplace, and committed by people of different classes— often unwittingly, and rarely with much regret. Insurance companies were surveyed, and data collected by interviews with insurance staff. It emerged that many insurers did not realise they had an insurance fraud problem, and those that did were either doing little to prevent it or were using ineffective methods. Insurance fraudsters are often given a great deal of help, often by officials who abuse the trust placed in them; insurers’ relationship with the police and with loss adjusters is not geared to stopping fraudsters, and insurance fraud is thus rendered easier. To illustrate this, and with the help of an insurance company, the researcher conducted a mock insurance fraud, and found it easy to commit. This study shows that insurance fraud is mostly an opportunistic crime. Within the study of crime prevention there is an approach which seeks to reduce the number of offences by curtailing the opportunities for crime. This is known as ‘situational crime prevention’, and is based on the ‘rational choice perspective’. Professor Ron Clarke, whose name is most closely associated with the approach, has called for more research to apply the principles and techniques of opportunity reduction to a range of crime types. This thesis represents an attempt to do this in relation to insurance fraud, and in so doing to stimulate ideas on how insurance fraud can be tackled effectively. In addition, it offers a new perspective on the situational approach and the techniques of opportunity reduction, plus a revised classification of these techniques. At the same time it offers a critique of the situational approach itself. The findings suggest that if fraud within the insurance industry is to be taken seriously then there are a range of structural concerns that need to be tackled, and that this moves beyond the scope of situational prevention.
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Domingues, Rémi. "Machine Learning for Unsupervised Fraud Detection." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181027.

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Fraud is a threat that most online service providers must address in the development of their systems to ensure an efficient security policy and the integrity of their revenue. Amadeus, a Global Distribution System providing a transaction platform for flight booking by travel agents, is targeted by fraud attempts that could lead to revenue losses and indemnifications. The objective of this thesis is to detect fraud attempts by applying machine learning algorithms to bookings represented by Passenger Name Record history. Due to the lack of labelled data, the current study presents a benchmark of unsupervised algorithms and aggregation methods. It also describes anomaly detection techniques which can be applied to self-organizing maps and hierarchical clustering. Considering the important amount of transactions per second processed by Amadeus back-ends, we eventually highlight potential bottlenecks and alternatives.
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Jurgovsky, Johannes. "Context-aware credit card fraud detection." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI109.

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La fraude par carte de crédit est devenue un problème majeur dans le secteur des paiements électroniques. Dans cette thèse, nous étudions la détection de fraude basée sur les données transactionnelles et abordons plusieurs de ces défis complexes en utilisant des méthodes d'apprentissage automatique visant à identifier les transactions frauduleuses qui ont été émises illégitimement au nom du titulaire légitime de la carte. En particulier, nous explorons plusieurs moyens d’exploiter les informations contextuelles au-delà des attributs de base d’une transaction, notamment au niveau de la transaction, au niveau de la séquence et au niveau de l'utilisateur. Au niveau des transactions, nous cherchons à identifier les transactions frauduleuses qui présentent des caractéristiques distinctes des transactions authentiques. Nous avons mené une étude empirique de l’influence du déséquilibre des classes et des horizons de prévision sur la performance d d'un classifieur de type random forest. Nous augmentons les transactions avec des attributs supplémentaires extraits de sources de connaissances externes et montrons que des informations sur les pays et les événements du calendrier améliorent les performances de classification, particulièrement pour les transactions ayant lieu sur le Web. Au niveau de la séquence, nous cherchons à détecter les fraudes qui sont difficiles à identifier en elles-mêmes, mais particulières en ce qui concerne la séquence à court terme dans laquelle elles apparaissent. Nous utilisons un réseau de neurone récurrent (LSTM) pour modéliser la séquence de transactions. Nos résultats suggèrent que la modélisation basée sur des LSTM est une stratégie prometteuse pour caractériser des séquences de transactions ayant lieu en face à face, mais elle n’est pas adéquate pour les transactions ayant lieu sur le Web. Au niveau de l'utilisateur, nous travaillons sur une stratégie existante d'agrégation d'attributs et proposons un concept flexible nous permettant de calculer de nombreux attributs au moyen d'une syntaxe simple. Nous fournissons une implémentation basée sur CUDA pour pour accélerer le temps de calcul de deux ordres de grandeur. Notre étude de sélection des attributs révèle que les agrégats extraits de séquences de transactions des utilisateurs sont plus utiles que ceux extraits des séquences de marchands. De plus, nous découvrons plusieurs ensembles d'attributs candidats avec des performances équivalentes à celles des agrégats fabriqués manuellement tout en étant très différents en termes de structure. En ce qui concerne les travaux futurs, nous évoquons des méthodes d'apprentissage artificiel simples et transparentes pour la détection des fraudes par carte de crédit et nous esquissons une modélisation simple axée sur l'utilisateur
Credit card fraud has emerged as major problem in the electronic payment sector. In this thesis, we study data-driven fraud detection and address several of its intricate challenges by means of machine learning methods with the goal to identify fraudulent transactions that have been issued illegitimately on behalf of the rightful card owner. In particular, we explore several means to leverage contextual information beyond a transaction's basic attributes on the transaction level, sequence level and user level. On the transaction level, we aim to identify fraudulent transactions which, in terms of their attribute values, are globally distinguishable from genuine transactions. We provide an empirical study of the influence of class imbalance and forecasting horizons on the classification performance of a random forest classifier. We augment transactions with additional features extracted from external knowledge sources and show that external information about countries and calendar events improves classification performance most noticeably on card-not-present transaction. On the sequence level, we aim to detect frauds that are inconspicuous in the background of all transactions but peculiar with respect to the short-term sequence they appear in. We use a Long Short-term Memory network (LSTM) for modeling the sequential succession of transactions. Our results suggest that LSTM-based modeling is a promising strategy for characterizing sequences of card-present transactions but it is not adequate for card-not-present transactions. On the user level, we elaborate on feature aggregations and propose a flexible concept allowing us define numerous features by means of a simple syntax. We provide a CUDA-based implementation for the computationally expensive extraction with a speed-up of two orders of magnitude. Our feature selection study reveals that aggregates extracted from users' transaction sequences are more useful than those extracted from merchant sequences. Moreover, we discover multiple sets of candidate features with equivalent performance as manually engineered aggregates while being vastly different in terms of their structure. Regarding future work, we motivate the usage of simple and transparent machine learning methods for credit card fraud detection and we sketch a simple user-focused modeling approach
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Books on the topic "Detection of insurance fraud"

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Office, Massachusetts Attorney General's. Fraud detection and prosecution: Combatting the "fraud tax". Boston, Mass.]: Attorney General, Commonwealth of Massachusetts, 1993.

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New York (State). Dept. of Audit and Control. Division of Management Audit. Non-profit health insurance companies, fraud prevention and detection activities. [Albany, N.Y: The Division, 1994.

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California. Bureau of State Audits. Department of Health Services: Use of its port of entry fraud detection programs is no longer justified. Sacramento, Calif. (555 Capitol Mall, Suite 300, Sacramento 95814): Bureau of State Audits, 1999.

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Institute, Pennsylvania Bar. Insurance fraud. [Mechanicsburg, Pa.] (5080 Ritter Rd., Mechanicsburg 17055-6903): Pennsylvania Bar Institute, 2003.

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Accounts, Great Britain Parliament House of Commons Committee of Public. Thirtieth report from the Committee of Public Accounts session 1984-85: Unemployment benefit service ; Prevention and detection of evasion of National Insurance contributions and of fraud and abuse relating to benefits paid by DHSS. London: HMSO, 1985.

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Albrecht, W. Steve. Fraud: Detection and prevention. New York (1211 Avenue of the Americas, New York 10036-8775): American Institute of Certified Public Accountants, 1996.

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Fraud: Prevention and detection. London: Butterworths, 1992.

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Lundy, William. Auto insurance fraud. [Basking Ridge, N.J.]: American Educational Institute, 2001.

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Soyer, Bariş. Marine insurance fraud. Milton Park, Abingdon, Oxon [UK]: Informa Law from Routledge, 2014.

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Young, Michael R. Financial Fraud Prevention and Detection. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118691748.

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Book chapters on the topic "Detection of insurance fraud"

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Shi, Yong, Yingjie Tian, Gang Kou, Yi Peng, and Jianping Li. "Health Insurance Fraud Detection." In Advanced Information and Knowledge Processing, 233–35. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-504-0_14.

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Bodaghi, Arezo, and Babak Teimourpour. "Automobile Insurance Fraud Detection Using Social Network Analysis." In Applications of Data Management and Analysis, 11–16. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95810-1_2.

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Hassan, Amira Kamil Ibrahim, and Ajith Abraham. "Modeling Insurance Fraud Detection Using Imbalanced Data Classification." In Advances in Intelligent Systems and Computing, 117–27. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27400-3_11.

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Mendoza-Tello, Julio C., Tatiana Mendoza-Tello, and Higinio Mora. "Blockchain as a Healthcare Insurance Fraud Detection Tool." In Research and Innovation Forum 2020, 545–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62066-0_41.

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Dua, Prerna, and Sonali Bais. "Supervised Learning Methods for Fraud Detection in Healthcare Insurance." In Intelligent Systems Reference Library, 261–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40017-9_12.

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Peng, Yi, Gang Kou, Alan Sabatka, Jeff Matza, Zhengxin Chen, Deepak Khazanchi, and Yong Shi. "Application of Classification Methods to Individual Disability Income Insurance Fraud Detection." In Computational Science – ICCS 2007, 852–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72588-6_136.

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Konijn, Rob M., and Wojtek Kowalczyk. "Finding Fraud in Health Insurance Data with Two-Layer Outlier Detection Approach." In Data Warehousing and Knowledge Discovery, 394–405. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23544-3_30.

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Sheshasaayee, Ananthi, and Surya Susan Thomas. "Usage of R Programming in Data Analytics with Implications on Insurance Fraud Detection." In International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018, 416–21. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03146-6_46.

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Sheshasaayee, Ananthi, and Surya Susan Thomas. "A Purview of the Impact of Supervised Learning Methodologies on Health Insurance Fraud Detection." In Advances in Intelligent Systems and Computing, 978–84. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7512-4_98.

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Pérez, Jesús M., Javier Muguerza, Olatz Arbelaitz, Ibai Gurrutxaga, and José I. Martín. "Consolidated Tree Classifier Learning in a Car Insurance Fraud Detection Domain with Class Imbalance." In Pattern Recognition and Data Mining, 381–89. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11551188_41.

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Conference papers on the topic "Detection of insurance fraud"

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Saldamli, Gokay, Vamshi Reddy, Krishna S. Bojja, Manjunatha K. Gururaja, Yashaswi Doddaveerappa, and Loai Tawalbeh. "Health Care Insurance Fraud Detection Using Blockchain." In 2020 Seventh International Conference on Software Defined Systems (SDS). IEEE, 2020. http://dx.doi.org/10.1109/sds49854.2020.9143900.

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Nur Prasasti, Iffa Maula, Arian Dhini, and Enrico Laoh. "Automobile Insurance Fraud Detection using Supervised Classifiers." In 2020 International Workshop on Big Data and Information Security (IWBIS). IEEE, 2020. http://dx.doi.org/10.1109/iwbis50925.2020.9255426.

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Peng, Jinfeng, Qingzhong Li, Hui Li, Lei Liu, Zhongmin Yan, and Shidong Zhang. "Fraud Detection of Medical Insurance Employing Outlier Analysis." In 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, 2018. http://dx.doi.org/10.1109/cscwd.2018.8465273.

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Anbarasi, M. S., and S. Dhivya. "Fraud detection using outlier predictor in health insurance data." In 2017 International Conference on Information Communication and Embedded Systems (ICICES). IEEE, 2017. http://dx.doi.org/10.1109/icices.2017.8070750.

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Peng, Yi, Gang Kou, Alan Sabatka, Zhengxin Chen, Deepak Khazanchi, and Yong Shi. "Application of Clustering Methods to Health Insurance Fraud Detection." In 2006 International Conference on Service Systems and Service Management. IEEE, 2006. http://dx.doi.org/10.1109/icsssm.2006.320598.

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Rawte, Vipula, and G. Anuradha. "Fraud detection in health insurance using data mining techniques." In 2015 International Conference on Communication, Information & Computing Technology (ICCICT). IEEE, 2015. http://dx.doi.org/10.1109/iccict.2015.7045689.

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Liu, Xi, Jian-Bo Yang, and Dong-Ling Xu. "Fraud detection in automobile insurance claims: A statistical review." In 14th International FLINS Conference (FLINS 2020). WORLD SCIENTIFIC, 2020. http://dx.doi.org/10.1142/9789811223334_0121.

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Turkeli, Serkan, Tahsin Eti, Yunus Guney, Mehmet Akyuz, Mustafa Alpkan Cicek, and Merve Cimen. "Enemy inside: salesperson fraud detection in the insurance industry." In 2020 15th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2020. http://dx.doi.org/10.23919/cisti49556.2020.9141105.

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Padhi, Slokashree, and Suvasini Panigrahi. "Decision Templates based Ensemble Classifiers for Automobile Insurance Fraud Detection." In 2019 Global Conference for Advancement in Technology (GCAT). IEEE, 2019. http://dx.doi.org/10.1109/gcat47503.2019.8978332.

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Rayan, Nirmal. "Framework for Analysis and Detection of Fraud in Health Insurance." In 2019 IEEE 6th International Conference on Cloud Computing and Intelligence Systems (CCIS). IEEE, 2019. http://dx.doi.org/10.1109/ccis48116.2019.9073700.

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Reports on the topic "Detection of insurance fraud"

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Ravikumar, B., Yuzhe Zhang, and David L. Fuller. Unemployment Insurance Fraud and Optimal Monitoring. Federal Reserve Bank of St. Louis, 2012. http://dx.doi.org/10.20955/wp.2012.024.

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Hogden, J. Maximum likelihood continuity mapping for fraud detection. Office of Scientific and Technical Information (OSTI), May 1997. http://dx.doi.org/10.2172/468619.

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Dutra, Lauren M., Matthew C. Farrelly, Brian Bradfield, Jamie Ridenhour, and Jamie Guillory. Modeling the Probability of Fraud in Social Media in a National Cannabis Survey. RTI Press, September 2021. http://dx.doi.org/10.3768/rtipress.2021.mr.0046.2109.

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Abstract:
Cannabis legalization has spread rapidly in the United States. Although national surveys provide robust information on the prevalence of cannabis use, cannabis disorders, and related outcomes, information on knowledge, attitudes, and beliefs (KABs) about cannabis is lacking. To inform the relationship between cannabis legalization and cannabis-related KABs, RTI International launched the National Cannabis Climate Survey (NCCS) in 2016. The survey sampled US residents 18 years or older via mail (n = 2,102), mail-to-web (n = 1,046), and two social media data collections (n = 11,957). This report outlines two techniques that we used to problem-solve several challenges with the resulting data: (1) developing a model for detecting fraudulent cases in social media completes after standard fraud detection measures were insufficient and (2) designing a weighting scheme to pool multiple probability and nonprobability samples. We also describe our approach for validating the pooled dataset. The fraud prevention and detection processes, predictive model of fraud, and the methods used to weight the probability and nonprobability samples can be applied to current and future complex data collections and analysis of existing datasets.
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