Medicare fraud poses a substantial challenge to healthcare systems, resulting in significant financial losses and undermining the quality of care provided to legitimate beneficiaries. This study investigates the use of machine learning (ML) to enhance Medicare fraud detection, addressing key challenges such as class imbalance, high-dimensional data, and evolving fraud patterns. A dataset comprising inpatient claims, outpatient claims, and beneficiary details was used to train and evaluate five ML models: Random Forest, KNN, LDA, Decision Tree, and AdaBoost. Data preprocessing techniques included resampling SMOTE method to address the class imbalance, feature selection for dimensionality reduction, and aggregation of diagnostic and procedural codes. Random Forest emerged as the best-performing model, achieving a training accuracy of 99.2% and validation accuracy of 98.8%, and F1-score (98.4%). The Decision Tree also performed well, achieving a validation accuracy of 96.3%. KNN and AdaBoost demonstrated moderate performance, with validation accuracies of 79.2% and 81.1%, respectively, while LDA struggled with a validation accuracy of 63.3% and a low recall of 16.6%. The results highlight the importance of advanced resampling techniques, feature engineering, and adaptive learning in detecting Medicare fraud effectively. This study underscores the potential of machine learning in addressing the complexities of fraud detection. Future work should explore explainable AI and hybrid models to improve interpretability and performance, ensuring scalable and reliable fraud detection systems that protect healthcare resources and beneficiaries.

Traditionally, the detection of fraudulent insurance claims relies on business rules and expert judgement which makes it a time-consuming and expensive process (\'Oskarsd\'ottir et al., 2022). Consequently, researchers have been examining ways to develop efficient and accurate analytic strategies to flag suspicious claims. Feeding learning methods with features engineered from the social network of parties involved in a claim is a particularly promising strategy (see for example Van Vlasselaer et al. (2016); Tumminello et al. (2023)). When developing a fraud detection model, however, we are confronted with several challenges. The uncommon nature of fraud, for example, creates a high class imbalance which complicates the development of well performing analytic classification models. In addition, only a small number of claims are investigated and get a label, which results in a large corpus of unlabeled data. Yet another challenge is the lack of publicly available data. This hinders not only the development of new methods, but also the validation of existing techniques. We therefore design a simulation machine that is engineered to create synthetic data with a network structure and available covariates similar to the real life insurance fraud data set analyzed in \'Oskarsd\'ottir et al. (2022). Further, the user has control over several data-generating mechanisms. We can specify the total number of policyholders and parties, the desired level of imbalance and the (effect size of the) features in the fraud generating model. As such, the simulation engine enables researchers and practitioners to examine several methodological challenges as well as to test their (development strategy of) insurance fraud detection models in a range of different settings. Moreover, large synthetic data sets can be generated to evaluate the predictive performance of (advanced) machine learning techniques.

In order to gain a better understanding of the background to this problem, let us first introduce some fundamental knowledge about machine learning. Compared with traditional programming, one major difference is that the reasoning behind the algorithm's decision-making is not defined by hard-coded rules which were explicitly programmed by a human, but it is rather learned by example data: thousands, sometimes millions of parameters get optimised without human intervention to finally capture a generalised pattern of the data. The resulting model allows to make predictions on new, unseen data with high accuracy. To illustrate the concept, let's consider a sample scenario about fraud detection in insurance claims. Verifying the legitimacy of an insurance claim is essential to prevent abuse.

Data science is a branch of information technology that deals with the analysis and processing of large volumes of data, which may be structured or unstructured or a mix of both, in order to find unseen patterns and derive meaningful information. Data science is useful to identify market opportunities, for process optimisation and cost reduction, and to identify abnormal financial transactions, among others. A typical project involves components that require expertise from several of these areas in combinations of varying proportions from one project, to another. As technology finds its way into all our daily activities, so do the digital data trails we leave behind, be it at retail outlets, banks and many other places. Many organisations have realised they are sitting on a veritable gold mine of information that they can capitalise on and put to good use.

This article is an extract from The Insurance and Reinsurance Law Review - Edition 10. The rapid development of converging technologies is bringing about fundamental changes to the insurance industry. In the long term, organisations that are slow to embrace these new technologies will struggle to compete and to retain their place in the market. In the insurance sector, the use of technology to innovate or disrupt is known as'insurtech'. This is an elastic term that takes in the use of new technologies by both start-ups and incumbent insurance companies to transform access to and analysis of data, build new products, drive customer engagement and squeeze inefficiencies from the current insurance model.

Cybersecurity is a growing area of risk in healthcare, and organizations are grappling with the vulnerabilities and the ways patient data can be used against patients and organizations. From identity theft to healthcare fraud, waste and abuse, cybercriminals breached 642 accounts of 500 or more patient profiles in 2020. That's a rate of more than 1.76 per day, reports HIPAA Journal, adding up to 29 million healthcare records breached last year. Security breaches cost healthcare companies $6 trillion dollars by the end of 2020. According to Health IT Security, three security data breaches in 2020 alone affected almost 2,000,000 records, opening opportunities for identity theft and online fraud.

Despite the amount of data that businesses collect, many of these are still hidden within the old-fashioned ways of analysis. Fraud Analytics is a process that involves gathering and storing vast amounts of data. This allows researchers to analyze the data to identify patterns and anomalies, as well as uncover hidden threats. Over the past decade, the demand for fraud detection has grown significantly in various industries. This is due to the increasing number of organizations wanting to identify fraud activities in their organizations.

The rapid development of converging technologies is bringing about fundamental changes to the insurance industry. In the long term, organisations that are slow to embrace these new technologies will struggle to compete and to retain their place in the market. In the insurance sector, the use of technology to innovate or disrupt is known as'insurtech'. This is an elastic term that takes in the use of new technologies by both start-ups and incumbent insurance companies to transform access to and analysis of data, build new products, drive customer engagement and squeeze inefficiencies from the current insurance model. Technologies such as telematics, the internet of things including smart home technologies, aerial imagery and drone technologies are giving insurers new ways to access data while developments in artificial intelligence (AI), machine learning and natural language processing are enabling insurers to process, analyse and gain insights from these large data sources.

To implement fair machine learning in a sustainable way, identifying the right fairness definition is key. However, fairness is a concept of justice, and various definitions exist. Some of them are in conflict with each other and there is no uniformly accepted notion of fairness. The most appropriate fairness definition for an artificial intelligence system is often a matter of application and the right choice depends on ethical standards and legal requirements. In the absence of officially binding rules, the objective of this document is to structure the complex landscape of existing fairness definitions. We propose the "Fairness Compass", a tool which formalises the selection process and makes identifying the most appropriate fairness metric for a given system a simple, straightforward procedure. We further argue that documenting the reasoning behind the respective decisions in the course of this process can help to build trust from the user through explaining and justifying the implemented fairness.

Successive lockdowns imposed across the globe and travel restrictions accelerated digital transformation at workplaces and for essential services. Unable to step out, people turned to online portals and apps for most tasks including shopping, learning and banking. Healthcare gained importance, driving more people to get insurance and firms also embraced digitisation to serve consumers, globally and in the Middle East. But interacting with consumers and verifying claims online, in order to ensure contactless service, has its challenges when cybercrime is surging as quickly as the tech-savvy economy. Saudi Arabia's cooperative insurer Tawuniya also found itself vulnerable, at a time when 95% firms in the kingdom were reportedly targeted by cybercrooks.