Health insurance fraud increases the inefficiency and inequality in our society. To address the widespread problem, cost effect techniques are in need to detect fraudulent claims. With a dataset from medical expense insurance in China, we propose a discrete choice model to identify predicting factors of fraudulent claims, and we address the major limitations of discrete choice model by consider...

This article aims at making a bridge between the theory of optimal auditing and current procedures applied to audit files in different markets where scoring is the instrument used to implement an audit strategy. The literature has not yet developed an optimal audit policy for the scoring methodology. Our application is meant for the audit of insurance fraud but can be applied to many other acti...

Fraud is an increasing phenomenon as shown in many surveys carried out by leading international consulting companies in the last years. Despite the evolution of electronic payments and hacking techniques there is still a strong human component in fraud schemes. Conflict of interest in particular is the main contributing factor to the success of internal fraud. In such cases anomaly detection to...

A data-driven Fraud detection model for insurance business can be seen as a two-phase method. Phase I is data-preprocessing of given dataset, in which, handling class imbalance major challenge. II that classification using Machine Learning models. It important to comprehend if there any influence the technique used on efficiency II. natural query intrigues one whether golden combination and spe...

Due to the dramatic increase of fraud which results in loss of billions of dollars worldwide each year, several modern techniques in detecting fraud are continually evolved and applied to many business fields. Fraud detection involves monitoring the behaviour of populations of users in order to estimate, detect, or avoid undesirable behaviour. Undesirable behaviour is a broad term including mis...

Anomaly detection is the identification of items, events or observations which do not conform to an expected pattern or other items in a dataset. Typically the anomalous items will translate to some kind of problem such as bank fraud, a structural defect, medical problems or finding errors in text. Anomalies are also referred to as outliers, novelties, noise, deviations and exceptions. Many tec...

As technology advances, fraud is becoming increasingly complicated and difficult to detect, especially when individuals collude. Surveys show that the median loss from collusive fraud is much greater than fraud perpetrated by individuals. Despite its prevalence and potentially devastating effects, internal auditors often fail to consider collusion in their fraud assessment and detection efforts...

Using data from a credit card issuer, a neural network based fraud detection system was trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity over a subsequent two-month period of time. The neural network was trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud,...