False positives occur regularly with traditional rule-based anti-fraud measures, where the system flags anything that falls outside a given set of parameters. For example, if you are planning a trip abroad and you start buying airline tickets and accommodation, this may trigger a fraud warning. A smarter system as described in the two previous paragraphs, that can better understand the underlying patterns of human behavior, could potentially use the new customer data (your travel purchases) to match you with a different cluster of users (for example, holiday travelers). It can then test your behavior against transactions typical to that of the new cluster of users, holiday travelers in this example, before automatically raising a fraud flag on your account.
Medical claims fraud is a major contributor to increased healthcare costs, but the negative impact can be lessened through effective fraud detection. In this paper, we combine Medicare provider utilization and payment data from 2012 to 2015 with corresponding fraud labels from the List of Excluded Individuals/Entities (LEIE) database. We demonstrate the effectiveness of detecting Medicare fraud with a limited number of known perpetrators, leading to severe class imbalance. For each of the three selected specialties, we use random undersampling to create four class distributions. Random Forest and Logistic Regression learners are built and evaluated based on fraud detection performance. Good fraud detection is demonstrated through the use of random undersampling, across three selected medical specialties. Statistically significant results are seen across the class distributions, with the 80:20 distribution having the best results. Overall, Random Forest (with either 100 or 500 trees), for each class distribution across all specialties, significantly outperforms Logistic Regression, with average AUC scores of 0.881 and 0.88, respectively.
Machine learning is a field of science that offers machines an ability to understand data and carry out processes just as a human would do. The ML technology uses complex algorithms to analyze large data sets and find data patterns that help in business decisions. This is why machine learning can detect fraud in the system easily. It is, in fact, used for various other purposes such as spam detection, product recommendation, image recognition, predictive analysis, etc. Gartner predicted that by the year 2022, the machines would be analyzing 50% of the data, which is only 10% more from the present scenario. Since machines are far better at detecting patterns, ML can analyze huge sets of data in one chance and find fraud-related behavior through cognitive technology.
Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post-implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti-money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention.
User-generated online reviews can play a significant role in the success of retail products, hotels, restaurants, etc. However,review systems are often targeted by opinion spammers who seek to distort the perceived quality of a product by creating fraudulent reviews. We propose a fast and effective framework, FRAUDEAGLE, for spotting fraudsters and fake reviews in online review datasets. Our method has several advantages: (1) it exploits the network effect among reviewers and products, unlike the vast majority of existing methods that focus on review text or behavioral analysis, (2) it consists of two complementary steps; scoring users and reviews for fraud detection, and grouping for visualization and sensemaking, (3) it operates in a completely unsupervised fashion requiring no labeled data, while still incorporating side information if available, and (4) it is scalable to large datasets as its run time grows linearly with network size. We demonstrate the effectiveness of our framework on syntheticand real datasets; where FRAUDEAGLE successfully reveals fraud-bots in a large online app review database.