Use of data mining in fraud

Fraud concept is a person, characteristic, entity, or event that represents a suspicious scenario and similar concepts can be grouped together to form a concept category.

Use of data mining in fraud

Open in a separate window 5. As we noted above, supervised methods are useful in detecting previously known patterns of fraud and abuse. In theory, we can apply unsupervised approaches to identify new types of fraud or abuse. Therefore, it can clear sequence and association rules between records, distinguish anomaly record s or group similar records.

They selected nine expense-related variables and compared them in two groups of fraudulent and non-fraudulent claims for building the detection models.

The input variables were average drug cost, average diagnosis fee, average amount claimed, average days of drug dispense, average medical expenditure per day, average consultation and treatment fees, average drug cost per day, average dispensing service fees and average drug cost per day.

They compared three data mining methods including logistic regressions, neural networks and classification trees for the detection of fraudulent or abusive behavior Liou et al. Research by Yang and Hwang used supervised data mining approach to assess whether the providers followed defined clinical pathways.

The indicators were amount of fee, number of cases, amount of prescription days, amount of visits per case, average consultation fee per case, average treatment fee per case, average drug fee per case, average fee per case, percentage of antibiotic prescriptions, and percentage of injection prescriptions.

They identified and ranked critical clusters using expert opinions about the importance of clusters in affecting health expenditures.

Finally, they illustrated managerial guidance based on expert opinions about the characteristics of each critical cluster Lin et al. This study gathered data from practitioner outpatient care claims submitted to a health insurance organization.

They calculated a risk score for indicating the degree of likelihood of abuse by the providers; and then classified providers using a decision tree Shin et al.

As advantages, Shin et al used a simple definition of anomaly score and extracted 38 features for detecting abuse. They also provided a detailed explanation of the data mining process.

They calculated one single measure, the Local Outlier Factor LOFindicating the degree of outlier-ness of each record. The complete definition and explanation of LOF can be found in the Breunig et al. In another study, association rules mining were applied to examine claims of specialist physicians Shan et al.

The data was organized in transactions which were defined as all the items claimed or billed for one patient on one day by one specialist.

on the use of data mining for the purp ose of o ccupational fraud detection, starting from a real world assumption, namely unsup ervised data, forces itself on. Aug 31,  · In the domain of health care fraud and abuse detection, supervised data mining involves methods that use samples of previously known fraudulent and non-fraudulent records. These two groups of records are used to construct models, which allow us to assign new observations to one of the two groups of records. Credit card fraud is a serious and growing problem. While predictive models for credit card fraud detection are in active use in practice, reported studies on the use of data mining approaches for credit card fraud detection are relatively few, possibly due to the lack of available data for research.

Association rules are statements of the form if antecedent s then consequent s. They identified association rules.

They considered the specialists whose claims frequently broke the extracted rules as those with a higher risk of fraudulent behavior Shan et al. A study by Sokol, Garcia, Rodriguez, West, and Johnson explains the introductory steps of preparing and visualizing the data. These steps should be followed in any data mining approach.

Usually these precursory steps need a large amount of work prior to the actual data mining. They used Health Care Financing Administration claims related to preventative services of mammography, bone density assessment and diabetic counseling Sokol et al.Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes.

Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more. The process of digging. Newly formed Securities and Exchange Commission task forces will explore the use of the SEC’s increased data-mining capability as a way to detect financial-reporting fraud at corporations, the.

Data mining to classify, cluster, and segment the data and automatically find associations and rules in the data that may signify interesting patterns, including those related to fraud.

Use of data mining in fraud

Expert systems to encode expertise for detecting fraud in the form of rules. Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. Using a broad range of techniques, you can use this information to increase revenues, cut costs, improve customer relationships, reduce risks and more.

Selection and peer-review under responsibility of Asociatia Grupul Roman de Cercetari in Finante Corporatiste doi: /S(15) ScienceDirect Emerging Markets Queries in Finance and Business Detecting and Preventing Fraud with Data Analytics Adrian Bănărescua,b,* aInstitutul de Economie Nationala, Calea 13 .

Aug 31,  · In the domain of health care fraud and abuse detection, supervised data mining involves methods that use samples of previously known fraudulent and non-fraudulent records. These two groups of records are used to construct models, which allow us to assign new observations to one of the two groups of records.

(PDF) Data Mining for Fraud Detection: Toward an Improvement on Internal Control Systems?