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Journal articles on the topic 'Detection of insurance fraud'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 February 2022

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1

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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3

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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4

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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5

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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7

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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8

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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9

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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10

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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11

Morley, Nicola J., Linden J. Ball, and Thomas C. Ormerod. "How the detection of insurance fraud succeeds and fails." Psychology, Crime & Law 12, no. 2 (April 2006): 163–80. http://dx.doi.org/10.1080/10683160512331316325.

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12

Zainal, Rafidah, Ayub Md Som, and Nafsiah Mohamed. "A REVIEW ON COMPUTER TECHNOLOGY APPLICATIONS IN FRAUD DETECTION AND PREVENTION." Management and Accounting Review (MAR) 16, no. 2 (December 31, 2017): 59. http://dx.doi.org/10.24191/mar.v16i2.671.

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Since the advancement in computer technology, fraud has become one of the high risk crimes in the world which needs urgent detection and prevention at early stage. Nowadays, different kinds of fraud exist in commercial areas namely; credit card, healthcare insurance, online reviews, telecommunications, automobile, etc. Fraud can also happen internally and externally in an organisation. In internal fraud an employee commits it individually, whereas the external fraud involves a wide range of schemes including a third party. Therefore, a detection and prevention mechanism system in the form of computer software is essential in preventing fraud so as to curb further financial losses. The aim of this paper is to critically review the use of computer technology to detect and prevent fraud in selected areas. Recent computer platforms used as a tool to develop fraud detection and prevention system are namely; spreadsheets, big data, forensic analytics, text analytics and expert systems. Based on the review, expert systems have been found to be the best option used as a future tool to curb fraud. The opportunities from this review may allow others to explore more on the technology implemented so as to improve fraud detection and prevention in the future.

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13

Gong, Jibing, Hekai Zhang, and Weixia Du. "Research on Integrated Learning Fraud Detection Method Based on Combination Classifier Fusion (THBagging): A Case Study on the Foundational Medical Insurance Dataset." Electronics 9, no. 6 (May 27, 2020): 894. http://dx.doi.org/10.3390/electronics9060894.

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In recent years, the number of fraud cases in basic medical insurance has increased dramatically. We need to use a more efficient method to identify the fraudulent users. Therefore, we deploy the cloud edge algorithm with lower latency to improve the security and enforceability in the operation process. In this paper, a new feature extraction method and model fusion technology are proposed to solve the problem of basic medical insurance fraud identification. The feature second-level extraction algorithm proposed in this paper can effectively extract important features and improve the prediction accuracy of subsequent algorithms. In order to solve the problem of unbalanced simulation allocation in the medical insurance fraud identification scenario, a sample division method based on the idea of sample proportion equilibrium is proposed. Based on the above methods of feature extraction and sample division, a new training and fitting model fusion algorithm (tree hybrid bagging, THBagging) is proposed. This method makes full use of the balanced idea of the tree model algorithm based on Boosting to fuse, and finally achieves the effect of improving the accuracy of basic medical insurance fraud identification.

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14

Mall, Sunita, Prasun Ghosh, and Parita Shah. "Management of Fraud: Case of an Indian Insurance Company." Accounting and Finance Research 7, no. 3 (April 29, 2018): 18. http://dx.doi.org/10.5430/afr.v7n3p18.

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Frauds in insurance are typically where a fraudster tries to gain undue benefit from the insurance contract by ignorance or wilful manipulation. Using the claims data in motor insurance obtained from a Mumbai based insurance company for the time period of 2010-2016, this study focuses on studying the pattern exhibited by those claims which have been rejected and accepted as well. The prime objective of the study is to identify the important or the significant triggers of fraud and predicting the fraudulent behaviour of the customers using the identified triggers in an existing algorithm. This study makes use of statistical techniques like logistic regression & CHAID (Chi Square Automatic Interaction Detection) technique to identify the significant fraud triggers and to determine the probability of rejection & acceptance of each claim coming in future respectively. Data mining techniques like decision tree and confusion matrix are used on the important parameters to find all possible combinations of these significant variables and the bucket for each combination.This study finds that variables like Seats/Tonnage, No Claim Bonus, Type of Vehicle, Gross Written Premium, Sum Insured, Discounts, State Similarity and Previous Insurance details are found to be significant at 1% level of significance. The variables like Branch Code and Risk Types are found to be significant at 5% level of signify cance. The Gain chart depicts that our model is a fairly good model. This research would help the insurance company in settling the legitimate claims within less time and less cost and would also help in identifying the fraudulent claims.

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Kirlidog, Melih, and Cuneyt Asuk. "A Fraud Detection Approach with Data Mining in Health Insurance." Procedia - Social and Behavioral Sciences 62 (October 2012): 989–94. http://dx.doi.org/10.1016/j.sbspro.2012.09.168.

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16

Tennyson, Sharon, and Pau Salsas-Forn. "Claims Auditing in Automobile Insurance: Fraud Detection and Deterrence Objectives." Journal of Risk & Insurance 69, no. 3 (September 2002): 289–308. http://dx.doi.org/10.1111/1539-6975.00024.

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17

Guan, Haowen, Bin Gong, and Yongchang Gao. "CODM: an outlier detection method for medical insurance claims fraud." International Journal of Computational Science and Engineering 1, no. 1 (2017): 1. http://dx.doi.org/10.1504/ijcse.2017.10008174.

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18

Gao, Yongchang, Haowen Guan, and Bin Gong. "CODM: an outlier detection method for medical insurance claims fraud." International Journal of Computational Science and Engineering 20, no. 3 (2019): 404. http://dx.doi.org/10.1504/ijcse.2019.103945.

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19

Dixon, Michael I. "Recent Initiatives in the Prevention and Detection of Insurance Fraud." Journal of Financial Crime 4, no. 3 (January 1997): 236–41. http://dx.doi.org/10.1108/eb025784.

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20

Muhammad, Saliu Adam, Xiangtao Chen, and Liao Bo. "Predictor Variables' Influence on Classification Outcome in Insurance Fraud Detection." International Journal of Hybrid Information Technology 8, no. 5 (May 31, 2015): 41–50. http://dx.doi.org/10.14257/ijhit.2015.8.5.05.

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21

Nian, Ke, Haofan Zhang, Aditya Tayal, Thomas Coleman, and Yuying Li. "Auto insurance fraud detection using unsupervised spectral ranking for anomaly." Journal of Finance and Data Science 2, no. 1 (March 2016): 58–75. http://dx.doi.org/10.1016/j.jfds.2016.03.001.

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22

Pustika Sukma, Dara, Adi Sulistiyono, and Widodo Tresno Novianto. "Fraud in Healthcare Service." SHS Web of Conferences 54 (2018): 03015. http://dx.doi.org/10.1051/shsconf/20185403015.

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In Indonesia, the fraud of healthcare service implementation occurs widely in hospitals, thereby harming the participants of social insurance. The objectives of research were to find out, to analyze, and to give solution to the fraud in the healthcare service. This research was taken place in several hospitals in Central Java Indonesia using non-doctrinal or empirical method on stakeholders related to national health insurance. The result of research showed that the substance of the ratification of Health Minister’s Regulation Number 36 of 2015 about Fraud Prevention in National Health Insurance in National Social Insurance System becomes the government’s attempt in suppressing fraud in healthcare service. In its structure, healthcare service occurs due to the pressure of enacted costing system, limited supervision, and justification in committing fraud and the imbalance between health service system and burden among clinicians, service provider not giving adequate incentive, inadequate medical equipment supply, system inefficiency, less transparency in health facilities, and cultural factor. Those who are responsible for the attempt of eradicating fraud such as Health Ministry, Regency/City Health Service, Hospital’s Board of Directors, Hospital Supervision Agency and Council, Social Insurance Administration Organization, professional organization, and Social Insurance participants should walk in the cycle starting from building awareness, reporting, detecting, investigating, sanction imposing, to building awareness.

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23

Khlevna, Iuliia L., and Bohdan S. Koval. "DEVELOPMENT OF THE AUTOMATED FRAUD DETECTION SYSTEM CONCEPT IN PAYMENT SYSTEMS." Applied Aspects of Information Technology 4, no. 1 (April 10, 2021): 37–46. http://dx.doi.org/10.15276/aait.01.2021.3.

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The paper presents the demand for the spread of payment systems. This is caused by the development of technology. The open issue of application of payment systems - fraud - is singled out. It is established that there is no effective algorithm that would be the standard for all financial institutions in detecting and preventing fraud. This is due to the fact that approaches to fraud are dynamic and require constant revision of forecasts. Prospects for the development of scientific and practical approaches to prevent fraudulent transactions in payment systems have been identified. It has been researched that machine learning is appropriate in solving the problem of detecting fraud in payment systems. At the same time, the detection of fraud in payment systems is not only to build the algorithmic core, but also to build a reliable automated system, which in real time, under high load, is able to control data flows and effectively operate the algorithmic core of the system. The paper describes the architecture, principles and operation models, the infrastructure of the automated fraud detection mechanism in payment systems. The expediency of using a cloud web service has been determined. The deployment of the model in the form of automated technology based on the Amazon Web Services platform is substantiated. The basis of the automated online fraud detection system is Amazon Fraud Detector and setting up payment fraud detection workflows in payment systems using a customizable Amazon A2I task type to verify and confirm high-risk forecasts. The paper gives an example of creating an anomaly detection system on Amazon DynamoDB streams using Amazon SageMaker, AWS Glue and AWS Lambda. The automated system takes into account the dynamics of the data set, as the AWS Lambda function also works with many other AWS streaming services. There are three main tasks that the software product solves: prevention and detection of fraud in payment systems, rapid fraud detection (counts in minutes), integration of the software product into the business where payment systems and services are used (for example, payment integration services in financial institutions, online stores, logistics companies, insurance policies, trading platforms, etc.). It is determined that the implementation of an automated system should be considered as a project. The principles of project implementation are offered. It is established that for the rational implementation of the project it is necessary to develop a specific methodology for the implementation of the software product for fraud detection in payment systems of business institutions.

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Gupta, Rohan Yashraj, Satya Sai Mudigonda, and Pallav Kumar Baruah. "TGANs with Machine Learning Models in Automobile Insurance Fraud Detection and Comparative Study with Other Data Imbalance Techniques." International Journal of Recent Technology and Engineering 9, no. 5 (January 30, 2021): 236–44. http://dx.doi.org/10.35940/ijrte.e5277.019521.

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A data-driven Fraud detection model for insurance business can be seen as a two-phase method. Phase I is data-preprocessing of a given dataset, in which, handling class imbalance is a major challenge. Phase II is that of classification using Machine Learning models. It is important to comprehend if there is any influence of the technique used in Phase I on the efficiency of the model used for Phase II. A natural query that intrigues one is whether there is a golden combination of a technique in Phase I and a specific model in Phase II for assured best performance of a Fraud Detection Model.In this work, we study a few techniques for handling data imbalance issue namely, SMOTE, MWMOTE, ADASYN and TGAN in combination with various classifier models like Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), LightGBM, XGBoost and Gradient Boosting Machines (GBM). The study is conducted on a dataset for motor vehicle insurance fraud detection.We present a comparison of various combinations of data imbalance technique and classifier models. It is observed that the combination of TGAN in Phase I and GBM in Phase II gives the best performance. This combination performs best in terms of important metrics such as false positive rate, precision and specificity. We obtained the lowest false positive rate of 0.0011 and precision of 0.9988 which minimizes the most critical risk for the insurance company of falsely classifying a non-fraud claim as a fraud. Finally, the specificity of 0.9989 indicates that the model was also very good at predicting the non-fraudulent claim.

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Salami, Suleiman, and Abass Wahab Olabamiji. "THE EFFECT OF FRAUD ON PROFITABILITY OF LISTED DEPOSIT MONEY BANKS IN NIGERIA." Malaysian Management Journal 25 (July 9, 2021): 169–90. http://dx.doi.org/10.32890/mmj2021.25.7.

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The increasing rate of fraud occurrence and poor profitability rate in the listed Deposit Money Banks (DMBs) in Nigeria calls for a research investigation. To unravel the likely connection between fraud and profitability, this study has examined the effect of fraud on the profitability of listed DMBs in Nigeria. To achieve this objective, the study adopted a correlational research design and utilised secondary data extracted from the Nigerian Deposit Insurance Commission (NDIC) and published financial statements of the DMBs. The study focused on 14 listed DMBs for a six-year period (2012-2017). Panel multiple regression technique was used to estimate the model of the study. The findings showed that fraud (proxied by actual loss from fraud and staff involvement in fraud) has a negative and significant effect on profitability (proxied by return on asset) of listed DMBs in Nigeria. In line with the findings, this study has recommended that listed DMBs should establish fraud detection mechanisms which will entail the setting up of an efficient, reliable and functioning fraud detection unit to monitor transactions that may be susceptible to fraud.

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Majhi, Santosh Kumar, Subho Bhatachharya, Rosy Pradhan, and Shubhra Biswal. "Fuzzy clustering using salp swarm algorithm for automobile insurance fraud detection." Journal of Intelligent & Fuzzy Systems 36, no. 3 (March 26, 2019): 2333–44. http://dx.doi.org/10.3233/jifs-169944.

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Subudhi, Sharmila, and Suvasini Panigrahi. "Detection of Automobile Insurance Fraud Using Feature Selection and Data Mining Techniques." International Journal of Rough Sets and Data Analysis 5, no. 3 (July 2018): 1–20. http://dx.doi.org/10.4018/ijrsda.2018070101.

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This article presents a novel approach for fraud detection in automobile insurance claims by applying various data mining techniques. Initially, the most relevant attributes are chosen from the original dataset by using an evolutionary algorithm based feature selection method. A test set is then extracted from the selected attribute set and the remaining dataset is subjected to the Possibilistic Fuzzy C-Means (PFCM) clustering technique for the undersampling approach. The 10-fold cross validation method is then used on the balanced dataset for training and validating a group of Weighted Extreme Learning Machine (WELM) classifiers generated from various combinations of WELM parameters. Finally, the test set is applied on the best performing model for classification purpose. The efficacy of the proposed system is illustrated by conducting several experiments on a real-world automobile insurance defraud dataset. Besides, a comparative analysis with another approach justifies the superiority of the proposed system.

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Darade, Baliram, Amol Bhatkal, Akshay Phadtare, and Ashish Katkade. "Fraud Detection in Health Care Insurance using Data Mining by Integrating Hospital and Health Insurance System." IJARCCE 6, no. 3 (March 30, 2017): 597–600. http://dx.doi.org/10.17148/ijarcce.2017.63139.

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Subudhi, Sharmila, and Suvasini Panigrahi. "Two-Stage Automobile Insurance Fraud Detection by Using Optimized Fuzzy C-Means Clustering and Supervised Learning." International Journal of Information Security and Privacy 14, no. 3 (July 2020): 18–37. http://dx.doi.org/10.4018/ijisp.2020070102.

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A novel two-stage automobile insurance fraud detection system is proposed that initially extracts a test set from the original imbalanced insurance dataset. A genetic algorithm based optimized fuzzy c-means clustering is then applied on the remaining data set for undersampling the majority samples by eliminating the outliers among them. Thereafter, the detection of the fraudulent claims occurs in two stages. In the first stage, each insurance record is passed to the clustering module that identifies the claim as genuine, malicious, or suspicious. The genuine and malicious samples are removed and only the suspicious instances are further scrutinized in the second stage by four trained supervised classifiers − Decision Tree, Support Vector Machine, Group Method for Data Handling and Multi-Layer Perceptron individually for final decision making. Extensive experiments and comparative analysis with another recent approach using a real-world automobile insurance dataset justifies the effectiveness of the proposed system.

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Artis, Manuel, Mercedes Ayuso, and Montserrat Guillen. "Detection of Automobile Insurance Fraud With Discrete Choice Models and Misclassified Claims." Journal of Risk & Insurance 69, no. 3 (September 2002): 325–40. http://dx.doi.org/10.1111/1539-6975.00022.

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Ganapathy, Apoorva. "Quantum Computing in High Frequency Trading and Fraud Detection." Engineering International 9, no. 2 (2021): 61–72. http://dx.doi.org/10.18034/ei.v9i2.549.

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Quantum Computing in high-frequency trading and fraud detection is an analysis of quantum computing and how it can be used by the different industries especially finance. It is an evolution of computing from the traditional computing method. Quantum computing is a process that is concentrated on creating systems and technology based on quantum theory rules. Quantum theory describes the energy on atomic and subatomic levels. Quantum computing uses quantum bits (qubits) which are more advanced than the traditional bits used by traditional computers. This article focuses on deploying quantum computers in solving problems that cannot be efficiently solved using traditional computers. In the finance sector, such as banking, insurance, and high-frequency trading, quantum computers can help optimize service by providing targeting and predictive analytics to reduce risk, provide personalized customer service, and provide the needed security framework against fraud.

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Kostyunina, Tatyana. "Classification of operational risks in construction companies on the basis of big data." MATEC Web of Conferences 193 (2018): 05072. http://dx.doi.org/10.1051/matecconf/201819305072.

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Nowadays, Big Data is commonly used in many business sectors. Its use is also relevant for the construction industry. One of the most promising areas of Big Data technologies application is their use for risk analysis and assessment. Big Data represents an efficient way to manage modern risks by analyzing the unlimited amount of structured and unstructured information. The study examines principles of operational risks classification in construction companies on the basis of Big Data technologies. The final goal of such classification is the creation of a solution pattern for subsequent use of Big Data. As an example, a solution pattern for such business problem as "Construction: Detection of Insurance Fraud" is created. Application of the Big Data analytics for fraud detection has a series of advantages as compared to traditional approaches. Insurance companies can build systems that include all relevant data sources. An analysis of operational risks by means of self-organizing Kohonen maps on the basis of the Deductor analytical platform is performed.

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Krambia Kapardis, Maria, and Konstantinos Papastergiou. "Fraud victimization in Greece: room for improvement in prevention and detection." Journal of Financial Crime 23, no. 2 (May 3, 2016): 481–500. http://dx.doi.org/10.1108/jfc-02-2015-0010.

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Purpose The purpose of this paper is to investigate fraud victimisation of Greek companies during the financial crisis years. Moreover, the paper seeks to encourage the implementation of proactive and reactive measures in an effort to minimize fraud victimisation. Design/methodology/approach Drawing on an extensive literature review and utilising a questionnaire administered by Krambia-Kapardis and Zopiatis (2010), auditors and management of companies who had fallen victim to fraud provided information on the typology of fraud and on proactive and reactive measures taken after a fraud incident had been reported to them. Both descriptive and inferential statistics were utilized to analyze the collected data and address the postulated research questions. Findings The survey has found that no industry or size of company is immune from fraud, with bigger companies and small- and medium-sized enterprises (SMEs) falling victim to industrial espionage and theft of cash and counterfeit, respectively. The banking and insurance sector appeared to be affected mainly by money laundering. Management fraud was mainly in the form of window dressing, whilst employee fraud involved predominately theft of cash and assets. Loss of reputation emerged as the main concern for the victim, and it had a determining impact on deciding not to report cases to the police. Research limitations/implications Because of the sensitive topic being investigated and despite having assured the respondents that their anonymity would be guaranteed, the respondents were hesitant in responding. Thus, the response rate was 16.4 per cent, slightly lower than a similar study carried out in Cyprus (Krambia-Kapardis and Zopiatis 2010). The findings, however, are considered to be reliable, given the fact that the respondents were individuals well versed with the topic under investigation and in a position to know if their company had fallen victim to fraud. Practical implications The findings have practical relevance to both industry stakeholders and academics who wish to further explore fraud victimization in the Greek business environment. Given that the financial crisis in Greece is continuing, fraud risk assessment ought to concentrate in the area of cash, and preventative measures need to be considered by the regulators and the victims. Originality/value Whilst fraud victimisation studies are becoming popular by the Big 4 accounting firms, there is no fraud victimisation study concentrating on the typology of fraud in Greece. With this survey, it will be possible to draw conclusions and make suggestions to the accounting profession on how to combat fraud, at a time, when the economic crisis is persisting and fraud is expected to escalate.

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Tang, Xiao-Bo, Wei Wei, Guang-Chao Liu, and Juan Zhu. "An Inference Model of Medical Insurance Fraud Detection: Based on Ontology and SWRL." KNOWLEDGE ORGANIZATION 44, no. 2 (2017): 84–96. http://dx.doi.org/10.5771/0943-7444-2017-2-84.

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Bayerstadler, Andreas, Linda van Dijk, and Fabian Winter. "Bayesian multinomial latent variable modeling for fraud and abuse detection in health insurance." Insurance: Mathematics and Economics 71 (November 2016): 244–52. http://dx.doi.org/10.1016/j.insmatheco.2016.09.013.

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Jekabsons, Gints, and Marina Uhanova. "Adaptive Regression and Classification Models with Applications in Insurance." Applied Computer Systems 15, no. 1 (July 1, 2014): 28–31. http://dx.doi.org/10.2478/acss-2014-0004.

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Abstract Nowadays, in the insurance industry the use of predictive modeling by means of regression and classification techniques is becoming increasingly important and popular. The success of an insurance company largely depends on the ability to perform such tasks as credibility estimation, determination of insurance premiums, estimation of probability of claim, detecting insurance fraud, managing insurance risk. This paper discusses regression and classification modeling for such types of prediction problems using the method of Adaptive Basis Function Construction

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Ekin, Tahir, and Paul Damien. "Analysis of Health Care Billing via Quantile Variable Selection Models." Healthcare 9, no. 10 (September 27, 2021): 1274. http://dx.doi.org/10.3390/healthcare9101274.

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Fraudulent billing of health care insurance programs such as Medicare is in the billions of dollars. The extent of such overpayments remains an issue despite the emerging use of analytical methods for fraud detection. This motivates policy makers to also be interested in the provider billing characteristics and understand the common factors that drive conservative and/or aggressive behavior. Statistical approaches to tackling this problem are confronted by the asymmetric and/or leptokurtic distributions of billing data. This paper is a first attempt at using a quantile regression framework and a variable selection approach for medical billing analysis. The proposed method addresses the varying impacts of (potentially different) variables at the different quantiles of the billing aggressiveness distribution. We use the mammography procedure to showcase our analysis and offer recommendations on fraud detection.

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Dhieb, Najmeddine, Hakim Ghazzai, Hichem Besbes, and Yehia Massoud. "A Secure AI-Driven Architecture for Automated Insurance Systems: Fraud Detection and Risk Measurement." IEEE Access 8 (2020): 58546–58. http://dx.doi.org/10.1109/access.2020.2983300.

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Kose, Ilker, Mehmet Gokturk, and Kemal Kilic. "An interactive machine-learning-based electronic fraud and abuse detection system in healthcare insurance." Applied Soft Computing 36 (November 2015): 283–99. http://dx.doi.org/10.1016/j.asoc.2015.07.018.

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Zhang, Yu, and Tyler Olson. "Statistical Modeling for Studying the Impact of ICD-10 on Health Fraud Detection." International Journal of Privacy and Health Information Management 5, no. 1 (January 2017): 111–31. http://dx.doi.org/10.4018/ijphim.2017010107.

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When an individual is seen or treated by a healthcare professional, a series of alphanumeric codes are used to describe the medical diagnoses and services provided. This designated classification structure, the ninth iteration of ICD (International Classification of Diseases), implements the use of coding for healthcare management, public health and medical informatics, and insurance purposes. ICD-9 has been the coding standard in the healthcare industry for 30 years. On October 1st, 2015, the tenth revision ICD-10 was formally implemented in the United States. This paper explores the validity of predictions from domain professionals regarding fraud detection and the implementation of the ICD-10 code set. The notion that fraud detection systems using supervised learning algorithms will encounter an initial decline in performance due to ICD-10 is fairly unsupported at the moment. The authors claim that the results from their study will provide evidence that will support this notion of a preliminary negative transitional impact.

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Panigrahi, Sapna, and Bhakti Palkar. "Comparative Analysis on Classification Algorithms of Auto-Insurance Fraud Detection based on Feature Selection Algorithms." International Journal of Computer Sciences and Engineering 6, no. 9 (September 30, 2018): 72–77. http://dx.doi.org/10.26438/ijcse/v6i9.7277.

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Mushunje, Leonard. "Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools." American Journal of Data Mining and Knowledge Discovery 4, no. 2 (2019): 70. http://dx.doi.org/10.11648/j.ajdmkd.20190402.13.

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Subudhi, Sharmila, and Suvasini Panigrahi. "Use of optimized Fuzzy C-Means clustering and supervised classifiers for automobile insurance fraud detection." Journal of King Saud University - Computer and Information Sciences 32, no. 5 (June 2020): 568–75. http://dx.doi.org/10.1016/j.jksuci.2017.09.010.

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Kaplan, Steven E., Danny Lanier, Kelly R. Pope, and Janet A. Samuels. "External Investigators' Follow-Up Intentions When Whistleblowers Report Healthcare Fraud: The Effects of Report Anonymity and Previous Confrontation." Behavioral Research in Accounting 32, no. 2 (July 15, 2020): 91–101. http://dx.doi.org/10.2308/bria-19-042.

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ABSTRACT Whistleblowing reports, if properly investigated, facilitate the early detection of fraud. Although critical, investigation-related decisions represent a relatively underexplored component of the whistleblowing process. Investigators are responsible for initially deciding whether to follow-up on reports alleging fraud. We report the results of an experimental study examining the follow-up intentions of highly experienced healthcare investigators. Participants, in the role of an insurance investigator, are asked to review a whistleblowing report alleging billing fraud occurring at a medical provider. Thus, participants are serving as external investigators. In a between-participant design, we manipulate the report type and whether the caller previously confronted the wrongdoer. We find that compared to an anonymous report, a non-anonymous report is perceived as more credible and follow-up intentions stronger. We also find that perceived credibility fully mediates the relationship between report type and follow-up intentions. Previous confrontation is not significantly associated with either perceived credibility or follow-up intentions. Data Availability: Data are available upon request.

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Nortey, Ezekiel N. N., Reuben Pometsey, Louis Asiedu, Samuel Iddi, and Felix O. Mettle. "Anomaly Detection in Health Insurance Claims Using Bayesian Quantile Regression." International Journal of Mathematics and Mathematical Sciences 2021 (February 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/6667671.

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Research has shown that current health expenditure in most countries, especially in sub-Saharan Africa, is inadequate and unsustainable. Yet, fraud, abuse, and waste in health insurance claims by service providers and subscribers threaten the delivery of quality healthcare. It is therefore imperative to analyze health insurance claim data to identify potentially suspicious claims. Typically, anomaly detection can be posited as a classification problem that requires the use of statistical methods such as mixture models and machine learning approaches to classify data points as either normal or anomalous. Additionally, health insurance claim data are mostly associated with problems of sparsity, heteroscedasticity, multicollinearity, and the presence of missing values. The analyses of such data are best addressed by adopting more robust statistical techniques. In this paper, we utilized the Bayesian quantile regression model to establish the relations between claim outcome of interest and subject-level features and further classify claims as either normal or anomalous. An estimated model component is assumed to inherently capture the behaviors of the response variable. A Bayesian mixture model, assuming a normal mixture of two components, is used to label claims as either normal or anomalous. The model was applied to health insurance data captured on 115 people suffering from various cardiovascular diseases across different states in the USA. Results show that 25 out of 115 claims (21.7%) were potentially suspicious. The overall accuracy of the fitted model was assessed to be 92%. Through the methodological approach and empirical application, we demonstrated that the Bayesian quantile regression is a viable model for anomaly detection.

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Akhmad, Arsandi, Lukas Lukas, and Bagus Mahawan. "Improving Performance Loan Fraud Model Prediction Using Mean Decrease Accuracy and Mean Decrease Gini." ACMIT Proceedings 6, no. 1 (July 6, 2021): 36–41. http://dx.doi.org/10.33555/acmit.v6i1.94.

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The purpose of this research is to develop a fraud detection model on loan transactions at failed banks in the context of deposit and deposit guarantees mandated to the Indonesia Deposit Insurance Corporation (IDIC). The data used in this study is the data of a bank in the Jakarta area that had liquidated at the end of 2015. Meanwhile, data on loan transaction ranges ranged from 2010 to 2015. This research also focuses on improving the performance of detection models by using feature selection. With the feature selection, it expected that the impact of the reduced performance of the model exposed to high variance and high bias due to the many features used can handled better.

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Viaene, Stijn, Richard A. Derrig, Bart Baesens, and Guido Dedene. "A Comparison of State-of-the-Art Classification Techniques for Expert Automobile Insurance Claim Fraud Detection." Journal of Risk & Insurance 69, no. 3 (September 2002): 373–421. http://dx.doi.org/10.1111/1539-6975.00023.

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Šubelj, Lovro, Štefan Furlan, and Marko Bajec. "An expert system for detecting automobile insurance fraud using social network analysis." Expert Systems with Applications 38, no. 1 (January 2011): 1039–52. http://dx.doi.org/10.1016/j.eswa.2010.07.143.

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Sun, Haixia, Jin Xiao, Wei Zhu, Yilong He, Sheng Zhang, Xiaowei Xu, Li Hou, Jiao Li, Yuan Ni, and Guotong Xie. "Medical Knowledge Graph to Enhance Fraud, Waste, and Abuse Detection on Claim Data: Model Development and Performance Evaluation." JMIR Medical Informatics 8, no. 7 (July 23, 2020): e17653. http://dx.doi.org/10.2196/17653.

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Background Fraud, Waste, and Abuse (FWA) detection is a significant yet challenging problem in the health insurance industry. An essential step in FWA detection is to check whether the medication is clinically reasonable with respect to the diagnosis. Currently, human experts with sufficient medical knowledge are required to perform this task. To reduce the cost, insurance inspectors tend to build an intelligent system to detect suspicious claims with inappropriate diagnoses/medications automatically. Objective The aim of this study was to develop an automated method for making use of a medical knowledge graph to identify clinically suspected claims for FWA detection. Methods First, we identified the medical knowledge that is required to assess the clinical rationality of the claims. We then searched for data sources that contain information to build such knowledge. In this study, we focused on Chinese medical knowledge. Second, we constructed a medical knowledge graph using unstructured knowledge. We used a deep learning–based method to extract the entities and relationships from the knowledge sources and developed a multilevel similarity matching approach to conduct the entity linking. To guarantee the quality of the medical knowledge graph, we involved human experts to review the entity and relationships with lower confidence. These reviewed results could be used to further improve the machine-learning models. Finally, we developed the rules to identify the suspected claims by reasoning according to the medical knowledge graph. Results We collected 185,796 drug labels from the China Food and Drug Administration, 3390 types of disease information from medical textbooks (eg, symptoms, diagnosis, treatment, and prognosis), and information from 5272 examinations as the knowledge sources. The final medical knowledge graph includes 1,616,549 nodes and 5,963,444 edges. We designed three knowledge graph reasoning rules to identify three kinds of inappropriate diagnosis/medications. The experimental results showed that the medical knowledge graph helps to detect 70% of the suspected claims. Conclusions The medical knowledge graph–based method successfully identified suspected cases of FWA (such as fraud diagnosis, excess prescription, and irrational prescription) from the claim documents, which helped to improve the efficiency of claim processing.

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Gepp, Adrian, J. Holton Wilson, Kuldeep Kumar, and Sukanto Bhattacharya. "A Comparative Analysis of Decision Trees Vis-`a-vis Other Computational Data Mining Techniques in Automotive Insurance Fraud Detection." Journal of Data Science 10, no. 3 (March 21, 2021): 537–61. http://dx.doi.org/10.6339/jds.201207_10(3).0010.

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