Fraud detection and frequent pattern matching in insurance claims using data mining techniques

Verma, Aayushi; Taneja, Anu; Arora, Anuja

doi:10.1109/ic3.2017.8284299

Cited by 33 publications

(25 citation statements)

References 9 publications

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“…No evidence of physician altruism was found, based on Finnish data. Thornton, Mueller, Schoutsen, and Hillegersberg (2013) Thornton et al, 2013and Verma, Taneja, & Arora, 2017, and Li, Huang, Jin, & Shi, 2008 provided a survey).…”

Section: Related Workmentioning

confidence: 99%

“…usnews.com/health-care/health-insurance/articles/2018-11-13/howto-avoid-medicare-scams.10 The 2015 Internet Crime Report formally defines identity theft as, "Someone steals and uses personal identifying information, like a name or Social Security number, without permission to commit fraud or other crimes, and/or (account takeover) a fraudster obtains account information to perpetrate fraud on existing accounts" p. 228.11 It is possible that both identity thefts and medical fraud result from a general weakness in institutions related to governance (seeGoel, 2019 for some evidence related to identity fraud).12 Additional details are available upon request.13 A related aspect, that we are unable to account for, may be the market power of medical professionals(McGuire, 2000). 14 The cross-sectional nature of the analysis prevents us from including state-specific dummy variables that might account for, among other things, the differences in enforcement/laws targeting nurses and physicians accused of fraud (see https://statelaws.findlaw.com/health-carelaws.html).15 See AlSaidi and Zeki (2019),Batra and Kundra (2019), andVerma et al (2017).…”

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confidence: 99%

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Medical professionals and health care fraud: Do they aid or check abuse?

Goel

2019

Manage Decis Econ

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This paper examines the role of health care professionals in combating health care fraud. It is not overall clear whether the share of the health professionals help control abuse. Using data across U.S. states, our econometric results show that greater employment of nurses consistently reduced health fraud, whereas more physicians did not have a significant impact. Further, more urbanized states and states with a greater proportion of the elderly experienced greater health scams. Identity thefts facilitated health care fraud, whereas different dimensions of health insurance (including the share of the population with Medicare, managed care, and no insurance) and hospital occupancy rates did not matter.

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Section: Related Workmentioning

confidence: 99%

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confidence: 99%

Medical professionals and health care fraud: Do they aid or check abuse?

Goel

2019

Manage Decis Econ

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“…Fraud detection and frequent pattern matching in insurance claims using data mining techniques [44] Kmeans clustering and association rule mining and Gaussian distribution.…”

Section: Study Name and Referencesmentioning

confidence: 99%

Sequence Mining and Prediction-Based Healthcare Fraud Detection Methodology

2020

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This paper presents a novel methodology to detect insurance claim related frauds in the healthcare system using concepts of sequence mining and sequence prediction. Fraud detection in healthcare is a non-trivial task due to the heterogeneous nature of healthcare records. Fraudsters behave as normal patients and with the passage of time keep on changing their way of planting frauds; hence, there is a need to develop fraud detection models. The sequence generation is not the part of previous researches which mostly focus on amount based analysis or medication versus diseases sequential analysis. The proposed methodology is able to generate sequences of services availed or prescribed by each specialty and analyse via two cascaded checks for the detection of insurance claim related frauds. The methodology addresses these challenges and self learns from historical medical records. It is based on two modules namely "Sequence rule engine and Prediction based engine". The sequence rule engine generates frequent sequences and probabilities of rare sequences for each specialty of the hospital. The comparison of such sequences with the actual patient sequences leads to the identification of anomalies as both sequences are not compliant to the sequences of the rule engine. The system performs further in detail analysis on all non-compliant sequences in the prediction based engine. The proposed methodology is validated by generating patient sequences from last five years transactional data of a local hospital and identifies patterns of service procedures administered to patients using Prefixspan algorithm and Compact prediction tree. Various experiments have been performed to validate the applicability of the developed methodology and the results demonstrate that the methodology is pertinent to detect healthcare frauds and provides on average 85% of accuracy. Thus can help in preventing fraudulent claims and provides better insight into how to improve patient management and treatment procedures.

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“…Many statistical techniques are also used to generate decision rules and k-means clustering is applied on a time series-based insurance claim data for the identification of anomalies and outliers. These disease-based outliers are used to detect the fraud related activities [20].…”

Section: Related Workmentioning

confidence: 99%

Medical Health Benefit Management System for Real-Time Notification of Fraud Using Historical Medical Records

et al. 2020

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This paper presents a novel framework for fraud detection in healthcare systems which self-learns from the historical medical data. Historical medical records are required for training and testing of machine learning models. The main problem being faced by both private and government health supported schemes is a rapid rise in the amount of claims by beneficiaries mostly based on fraudulent billing. Detection of fraudulent transactions in healthcare systems is a strenuous task due to intricate relationships among dynamic elements including doctors, patients, service. In light of aforementioned challenges in health support programs, there is a need to develop intelligent fraud detection models for tracing the loopholes in procedures which may lead to successful reimbursement of fraudulent medical bills. In order to address the issue of fraud in healthcare programs our solution proposes a framework based on three entities (patient, doctor, service). Firstly, the framework computes association scores for three elements of the healthcare ecosystem namely patients, doctors or services. The framework filters out identified cases using association scores. The Confidence values, after G-means clustering of transactional data, are computed for each service in each specialty. Rules are generated based on the confidence values of services for each specialty. Then, an evaluation of identified cases is done using rule engine. The framework classifies cases into fraudulent activities based on the similarity bit’s value. The validation of framework is performed on local hospital employees transactional data which includes many reported cases of fraudulent activities in addition to some introduced anomalies.

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Fraud detection and frequent pattern matching in insurance claims using data mining techniques

Cited by 33 publications

References 9 publications

Medical professionals and health care fraud: Do they aid or check abuse?

Medical professionals and health care fraud: Do they aid or check abuse?

Sequence Mining and Prediction-Based Healthcare Fraud Detection Methodology

Medical Health Benefit Management System for Real-Time Notification of Fraud Using Historical Medical Records

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