A TAXONOMY OF MACHINE LEARNING-BASED FRAUD DETECTION SYSTEMS

As fundamental changes in information systems drive digitalization, the heavy reliance on computers today significantly increases the risk of fraud. Existing literature promotes machine learning as a potential solution approach for the problem of fraud detection as it is able able to detect patterns in large datasets efficiently. However, there is a lack of clarity and awareness on which components and functionalities of machine learning-based fraud detection systems exist and how these systems can be classified consistently. We draw on 54 identified relevant machine learning-based fraud detection systems to address this research gap and develop a taxonomic scheme. By deriving three archetypes of machine learning-based fraud detection systems, the taxonomy paves the way for research and practice to understand and advance fraud detection knowledge to combat fraud and abuse

Categories of Approaches for IT Security Investment Decisions: A systematic literature review

The repurposing of problem-solving artifacts is an efficient way to innovate. Originating in evolutional theory, exaptation – the repurposing of an existing trait – gained recently attention in IS research due to the generative and malleable characteristics of digital technologies. Notwithstanding, research on this theoretical construct in IS research is scarce, while the innovation and economics literature already adapted the theory to, e.g., explain and predict disruptive market behaviors. With a scoping literature review, this paper pursues to draw a comprehensive picture of the current state of research of exaptation in IS research. Through an analysis of 46 publications, we could structure the field, derive three valuable contributions for the general exaptation theory and outline a future research agenda provide orientation and inspiration for further exaptation research in the digital and organizational context

Show Me Your Claims and I'll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data

Health insurance claim fraud is a serious issue for the healthcare industry as it drives up costs and inefficiency. Therefore, claim fraud must be effectively detected to provide economical and high-quality healthcare. In practice, however, fraud detection is mainly performed by domain experts resulting in significant cost and resource consumption. This paper presents a novel Convolutional Neural Network-based fraud detection approach that was developed, implemented, and evaluated on Medicare Part B records. The model aids manual fraud detection by classifying potential types of fraud, which can then be specifically analyzed. Our model is the first of its kind for Medicare data, yields an AUC of 0.7 for selected fraud types and provides an applicable method for medical claim fraud detection

Show Me Your Claims and I\u27ll Tell You Your Offenses: Machine Learning-Based Decision Support for Fraud Detection on Medical Claim Data

Health insurance claim fraud is a serious issue for the healthcare industry as it drives up costs and inefficiency. Therefore, claim fraud must be effectively detected to provide economical and high-quality healthcare. In practice, however, fraud detection is mainly performed by domain experts resulting in significant cost and resource consumption. This paper presents a novel Convolutional Neural Network-based fraud detection approach that was developed, implemented, and evaluated on Medicare Part B records. The model aids manual fraud detection by classifying potential types of fraud, which can then be specifically analyzed. Our model is the first of its kind for Medicare data, yields an AUC of 0.7 for selected fraud types and provides an applicable method for medical claim fraud detection

How to Avoid Medication Errors: Investigating the Roles of Policies and Nudging from A Stress Perspective

According to the World Health Organization (WHO), one of the most frequently occurring error types in healthcare are medication errors which arise due to manual data transfers and time pressure when transferring the data. Errors that occur during this manual procedure often go unnoticed and can have far-reaching health-consequences for patients. To avoid human errors, the healthcare sector often relies on guidelines and policies. However, research from the field of information security found policies to be additionally increasing professionals’ stress. Therefore, we aim to investigate how nudging can help to foster medical professionals’ compliance without causing stress due to regulations

At What Price? Exploring the Potential and Challenges of Differentially Private Machine Learning for Healthcare

The increased generation of data has become one of the main drivers of technological innovation in healthcare. This applies in particular to the adoption of Machine Learning models that are used to generate value from the growing available healthcare data. However, the increased processing of sensitive healthcare data comes with challenges in terms of data privacy. Differential privacy, the method of adding randomness to the data to increase privacy, has gained popularity in the last few years as a possible solution. However, while the addition of randomness increases privacy, it also reduces overall model performance, generating a privacy-utility trade-off. Examining this trade-off, we contribute to the literature by providing an empirical paper that experimentally evaluates two prominent and innovative methods of differentially private Machine Learning on medical image and text data to deepen the understanding of the existing potential and challenges of such methods for the healthcare domain

Healthcare in Fraudster\u27s Crosshairs: Designing, Implementing and Evaluating a Machine Learning Approach for Anomaly Detection on Medical Prescription Claim Data

Health insurance claim fraud is a serious problem for the health care industry. As it drives up costs and inefficiency, claim fraud must be effectively detected to provide economic and high-quality healthcare. In this paper, we present a hybrid machine learning approach that was developed, implemented, and evaluated in the context of a prescription claim service provider with data of over one million prescription claims. It combines the advantages of supervised and unsupervised anomaly detection. Based on prescription claim data as well as metadata of providers and patients, we design a data preprocessing pipeline and combine a Random Forest with an Autoencoder. The resulting hybrid model is evaluated against previous research and standard supervised and unsupervised algorithms. Our model outperforms these baseline models with an AUC of 0.8253 and provides an applicable method for medical prescription fraud detectio

A Process-Based Approach to Information Security Investment Evaluation: Design, Implementation, and Evaluation

In recent years, the importance of information security has grown significantly due to the rise of cyber threats and attacks. However, evaluating investments in information security can be challenging, as traditional methods often rely solely on monetary factors and fail to capture the dynamic nature of business processes. This paper introduces a novel process-based evaluation method for assessing the effect of investments in information security on business processes. The paper outlines practical design requirements for the method and its instantiation as a prototype, which is then evaluated using a three-step approach with two companies from the healthcare and energy sectors. The evaluation results demonstrate the proposed method\u27s usefulness in information security investment decisions. This paper contributes to the field of information security investment evaluation by providing a proof-of-concept that potentially paves the way for future research to increase the quality and economics of investments in information security