Adversarial Outlier Detection Methods for Health Care Fraud

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ekin, Tahir

Abstract: Outlier detection is widely used for identification of unusual suspicious patte ... Expand

Abstract: Outlier detection is widely used for identification of unusual suspicious patterns in health care fraud detection. However, the impact of adversarial perturbations on outliers and the related decisions are not well studied. In the health care fraud detection domain, standard practice does not consider health care billings being subject to intentional data manipulations. This paper presents a decision theoretic approach for outlier detection in adversarial environments. Proposed adversarial risk analysis-based framework allows incomplete information and adversarial perturbations on the data inputs. While the work with actual health care fraud data is ongoing, the proposed novel adversarial outlier detection method has the potential to support health care fraud audit decision support systems. KeywordsAdversarial unsupervised learning, adversarial risk analysis, health care fraud, fraud analytics, Bayesian. Collapse

Topics: fraud detection

Methods: anomaly detection unsupervised learning Monte Carlo simulation machine learning

Theories: game theory

Detection of Stock Manipulation Influencer Content using Supervised Learning

2023 | International Conference on Information Systems | Citations: 0

Authors: Haase, Frederic; Rath, Oliver; Lauten, Julia; Schoder, Detlef

Abstract: In recent years, social media influencers have emerged as key players in stock ... Expand

Abstract: In recent years, social media influencers have emerged as key players in stock manipulation schemes. Despite their growing impact, methods to detect such activities remain scarcely explored. In this study, we examine the social media content of stock manipulation influencers (SMIs) implicated in a $100 million fraud case by the U.S. Securities and Exchange Commission (SEC) in 2022. Leveraging natural language processing (NLP) techniques, we first investigate the linguistic characteristics present in the social media content published by SMIs. Next, we develop and evaluate supervised learning models to detect manipulative content. Our results have significant implications for investors, regulators, and the broader financial community. They reveal the unique linguistic characteristics of SMI content and demonstrate the potential of machinelearning and deep-learning-based techniques in advancing fraud detection systems. Collapse

Topics: Twitter social media fraud detection database system social media analytics

Methods: machine learning RoBERTa DistilBERT computational algorithm natural language processing

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

2023 | Information Systems Frontiers | Citations: 0

Authors: Hajek, Petr; Abedin, Mohammad Zoynul; Sivarajah, Uthayasankar

Abstract: Mobile payment systems are becoming more popular due to the increase in the numb ... Expand

Abstract: Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems. Collapse

Topics: fraud detection mobile system mobile payment electronic payment Python

Methods: XGBoost machine learning anomaly detection supervised learning experiment

Black-box Models’ Explainability: A Theoretical and Practical Perspective

2023 | Americas Conference on Information Systems | Citations: 0

Authors: He, Yuanqi; Gu, Bin; Li, Chunxiao; Yan, Lingling; Ding, Yanqing

Abstract: The lack of explainability remains a critical challenge to the widespread adopt ... Expand

Abstract: The lack of explainability remains a critical challenge to the widespread adoption of artificial intelligence (AI) in many fields. "Understanding brings in trust", while machine-learning models offer superior prediction accuracy, understanding the underlying logic is equally important to foster trust in these models. In this paper, we present eXplainable AI (XAI) as a solution to this challenge. Our research focuses on three key aspects of XAI: mathematics, humanities and social sciences, and practical applications. We demonstrated the feasibility of XAI through the use of artificially-constructed and model-derived ground truth, and verified performances of different XAIs. We also explored three dimensions of explainable consistency and emphasized the significance of human-machine consistency. Finally, we applied our research to a real-world scenario by cooperating with a national bank in China. Our findings highlight that XAI is both mathematically and practically meaningful, but more efforts need to be dedicated to this human-machine communication field. Collapse

Topics: explainable artificial intelligence shapley additive explanation local interpretable model agnostic explanation decision making accounting

Methods: computational algorithm principal component analysis data transformation XGBoost experiment

Key Factors in Achieving Service Level Agreements (SLA) for Information Technology (IT) Incident Resolution

2023 | Information Systems Frontiers | Citations: 0

Authors: Swain, Ajaya K.; Garza, Valeria R.

Abstract: In this paper, we analyze the impact of various factors on meeting service level ... Expand

Abstract: In this paper, we analyze the impact of various factors on meeting service level agreements (SLAs) for information technology (IT) incident resolution. Using a large IT services incident dataset, we develop and compare multiple models to predict the value of a target Boolean variable indicating whether an incident met its SLA. Logistic regression and neural network models are found to have the best performance in terms of misclassification rates and average squared error. From the best-performing models, we identify a set of key variables that influence the achievement of SLAs. Based on model insights, we provide a thorough discussion of IT process management implications. We suggest several strategies that can be adopted by incident management teams to improve the quality and effectiveness of incident management processes, and recommend avenues for future research. Collapse

Topics: missing data analytical information system ITIL corporate network security logistics management

Methods: support vector machine artificial neural network machine learning regression analysis method logistic regression

Theories: information systems theory

CATCHM: A novel network-based credit card fraud detection method using node representation learning

2023 | Decision Support Systems | Citations: 0

Authors: Van Belle, Rafaël; Baesens, Bart; De Weerdt, Jochen

Abstract: Advanced fraud detection systems leverage the digital traces from (credit-card) ... Expand

Abstract: Advanced fraud detection systems leverage the digital traces from (credit-card) transactions to detect fraudulent activity in future transactions. Recent research in fraud detection has focused primarily on data analytics combined with manual feature engineering, which is tedious, expensive and requires considerable domain expertise. Furthermore, transactions are often examined in isolation, disregarding the interconnection that exists between them. Collapse

Topics: fraud detection credit card fraud privacy business process management electronic mail

Methods: computational algorithm experiment machine learning feature engineering literature study

Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention

2023 | Decision Support Systems | Citations: 0

Authors: Wang, Gang; Ma, Jingling; Chen, Gang

Abstract: Financial statement fraud caused by listed companies directly jeopardizes the re ... Expand

Abstract: Financial statement fraud caused by listed companies directly jeopardizes the reliability the financial reporting process. Leveraging multimodal information for financial statement fraud detection (FSFD) has recently become of great interest to academic research and industrial applications. Unfortunately, the predictive ability of multi­ modal information in FSFD remains largely underexplored, particularly the fusion ambiguity embedded in and among multi-modalities. In this study, we propose a novel attention-based multimodal deep learning method, named RCMA, toward an accurate FSFD. RCMA synthesizes a fine-grained attention mechanism including three innovative attention modules, i.e., ratio-aware attention, chapter-aware attention, and modality-aware attention mechanism. The first two attention mechanisms help to liberate the extraordinary predictive power of the financial modality and the textual modality on FSFD, respectively. Moreover, the proposed modality-aware attention mechanism enables better coordination between the two modalities. Furthermore, to ensure effec­ tive learning on the attention-based multimodal embedding, we design a novel loss function named Focal and Consistency Loss, or FCL. It considers class-imbalance and modality-consistency simultaneously, to specialize the optimization of FSFD. The experimental results on the real-world dataset show that the proposed RCMA on FSFD task outperformed the state-of-the-art benchmarks. Furthermore, interpretation analysis visualizes the attention weights of different ratio groups, chapters, and modalities from RCMA, and illustrates how these interpretations influence stakeholders’ decision process for FSFD. Collapse

Topics: accounting fraud detection database system

Methods: machine learning deep learning financial report robustness check experiment

Efficient fraud detection using deep boosting decision trees

2023 | Decision Support Systems | Citations: 0

Authors: Xu, Biao; Wang, Yao; Liao, Xiuwu; Wang, Kaidong

Abstract: Fraud detection is to identify, monitor, and prevent potentially fraudulent acti ... Expand

Abstract: Fraud detection is to identify, monitor, and prevent potentially fraudulent activities from complex data. The recent development and success in AI, especially machine learning, provides a new data-driven way to deal with fraud. From a methodological point of view, machine learning based fraud detection can be divided into two categories, i.e., conventional methods (decision tree, tree boosting methods, etc.) and deep learning, both of which have significant limitations in terms of the lack of representation learning ability for the former and interpretability for the latter. Furthermore, due to the rarity of detected fraud cases, the associated data is usually imbalanced, which seriously degrades the performance of classification algorithms. Collapse

Topics: fraud detection marketing management

Methods: machine learning computational algorithm decision tree classification artificial neural network gradient boosting

On the potentials of quantum computing – An interview with Heike Riel from IBM Research

2022 | Electronic Markets | Citations: 0

Authors: Alt, Rainer

Abstract: In this interview, Dr. Heike Riel, a leading scientist and Fellow at IBM Researc ... Expand

Abstract: In this interview, Dr. Heike Riel, a leading scientist and Fellow at IBM Research, reports on the current state of research in the field of quantum computing. Building on the distinction of gateable quantum computers and quantum annealers, the interview sheds light on how research has evolved on gateable quantum computers, which are the path developed by IBM. These gateable quantum computers are described with their current status as well as the improvements and challenges regarding speed, scale, and quality. All three parameters are important for increasing the performance of these universal quantum computers and for leveraging their potentials compared to classical computers. In particular, complex mathematical problems being present in numerous applications in science and business may be solved. Among the examples mentioned are optimization problems that tend to scale exponentially with the number of parameters, for example, in material and natural sciences or simulation problems in the financial industry and in manufacturing. The interview concludes with a critical assessment of possible risks and expectations for the future. Collapse

Topics: quantum computing computer hardware competitive advantage innovation management digital innovation

Methods: computational algorithm machine learning application qualitative interview

Introduction to the Blockchain Engineering Minitrack

2022 | HICSS | Citations: 0

Authors: Chen, Hong-Mei; Sanders, Lawrence

Abstract: ... Expand

Abstract: Collapse

Topics: blockchain smart contract malware online advertising click fraud

Methods: experiment