Fraud detection using outlier predictor in health insurance data

Anbarasi, M. S.; Dhivya, S.

doi:10.1109/icices.2017.8070750

Cited by 18 publications

(11 citation statements)

References 7 publications

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“…To detect fraud, an effective way is to detect outliers. Data deviations from the pattern can usually be known to reduce losses that can occur [6].…”

Section: Methodsmentioning

confidence: 99%

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Outlier Detection in Inpatient Claims Using DBSCAN and K-Means

Sari

Suharjito

2022

j. Teknik inform.

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Health insurance helps people to obtain quality and affordable health services. The claim billing process is manually input code to the system, this can lack of errors and be suspected of being fraudulent. Claims suspected of fraud are traced manually to find incorrect inputs. The increasing volume of claims causes a decrease in the accuracy of tracing claims suspected of fraud and consumes time and energy. As an effort to prevent and reduce the occurrence of fraud, this study aims to determine the pattern of data on the occurrence of fraud based on the formation of data groupings. Data was prepared by combining claims for inpatient bills and patient bills from hospitals in 2020. Two methods were used in this study to form clusters, DBSCAN and KMeans. To find out the outliers in the cluster, Local Outlier Factor (LOF) was added. The results from experiments show that both methods can detect outlier data and distribute outlier data in the formed cluster. Variable that high effect becomes data outlier is the length of stay, claims code, and condition of patient when discharged from the hospital. Accuracy K-Means is 0.391, 0.003 higher than DBSCAN, which is 0.389.

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“…To detect fraud, an effective way is to detect outliers. Data deviations from the pattern can usually be known to reduce losses that can occur [6].…”

Section: Methodsmentioning

confidence: 99%

“…The results of this figure reveal that the sensitivity to the proposed framework is very high and can detect most of the inappropriate data in the data set. Alternatively, specifically the true negative value, varies between 52.8% and 83.4% [6].…”

Section: Introductionmentioning

confidence: 99%

Outlier Detection in Inpatient Claims Using DBSCAN and K-Means

Sari

Suharjito

2022

j. Teknik inform.

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“…The traditional approach to fraud detection is based on the comprehensive development of fraud indicators. Automotive insurance is the most established type of insurance fraud because it originates from false accident claims (Anbarasi & Dhivya, 2017). Several studies have employed machine learning methods to detect fraudulent claims by health facility providers, e.g., the decision tree, Support Vector Machine (SVM), and Back Propagation Neural Network (BPNN) (Chen & Chen, 2020), also random forest and XGBoost (Akbar et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

“…One problem in detecting fraud using machine learning is the imbalanced distributions of data in each class (Chen & Chen, 2020), where one may have significantly more data than another. In fraud insurance detection, the fraud class is usually the minority, so the model tends to produce a bias and is not feasible to implement since the prediction is typically inaccurate for the minority class (Anbarasi & Dhivya, 2017). Oversampling is one solution to deal with imbalanced data.…”

Section: Introductionmentioning

confidence: 99%

Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim

Nugraha¹,

Pardede²,

Subekti³

2022

KJS publishes peer-review articles in Mathematics, Computer Sci

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Fraud on health insurance impacts cost overruns and a quality decline in health services in the long term. The use of machine learning to detect fraud on health insurance is increasingly popular. However, one challenge in predicting health insurance fraud is the data imbalance. The data imbalance can cause a bias towards the majority class in many machine learning methods. Oversampling is a solution for data imbalance by augmenting new data based on the existing minority class data. Recently, there has been growing interest in employing deep learning for data augmentation. One of them is using Generative Adversarial Networks (GAN). This paper proposes using GAN as an oversampling method to generate additional data for minority classes. Since data for detecting health insurance fraud are tabular, we adopt Conditional Tabular GAN (CTGAN) architecture where the generator is conditioned to adjust the tabular data input and receive additional information to produce samples according to the specified class conditions. The new balanced data are used to train 17 classification algorithms. Our experiments showed that the proposed method performs better than other oversampling methods on several evaluation metrics, i.e., accuracy, precision score, F1-score, and ROC.

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“…Many authors have utilized the concept of 'graph theory' for connecting the patients, diseases and medicines. Most of the times, the studies are supplemented with the prior knowledge of the medicines that were being used for the various diseases and they established a correlation between the reference set (the original knowledge) and the candidate set (the extracted knowledge) [27,28]. 4.…”

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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 using outlier predictor in health insurance data

Cited by 18 publications

References 7 publications

Outlier Detection in Inpatient Claims Using DBSCAN and K-Means

Outlier Detection in Inpatient Claims Using DBSCAN and K-Means

Oversampling based on generative adversarial networks to overcome imbalance data in predicting fraud insurance claim

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

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