A Service Architecture Using Machine Learning to Contextualize Anomaly Detection

Laughlin, Brandon; Sankaranarayanan, Karthik; El-Khatib, Khalil

doi:10.4018/jdm.2020010104

Cited by 10 publications

(8 citation statements)

References 42 publications

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“…Within XAI literature, researchers have tried to assess the impact of explanations on the decision-making of fraud experts working with AI models in domains such as intrusion detection, fraudulent warranty claims, and banking transaction frauds. In [38], the authors provide a service architecture for security experts with explanations, aiming This is an open access post-print version; the final authenticated version is available online at https://link.springer.com/chapter/10.1007/978-3-030-57321-8_18 by © IFIP International Federation for Information Processing 2020.…”

Section: Related Workmentioning

confidence: 99%

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Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

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Explainable Artificial Intelligence (XAI) develops technical explanation methods and enable interpretability for human stakeholders on why Artificial Intelligence (AI) and machine learning (ML) models provide certain predictions. However, the trust of those stakeholders into AI models and explanations is still an issue, especially domain experts, who are knowledgeable about their domain but not AI inner workings. Social and user-centric XAI research states it is essential to understand the stakeholder's requirements to provide explanations tailored to their needs, and enhance their trust in working with AI models. Scenariobased design and requirements elicitation can help bridge the gap between social and operational aspects of a stakeholder early before the adoption of information systems and identify its real problem and practices generating user requirements. Nevertheless, it is still rarely explored the adoption of scenarios in XAI, especially in the domain of fraud detection to supporting experts who are about to work with AI models. We demonstrate the usage of scenario-based requirements elicitation for XAI in a fraud detection context, and develop scenarios derived with experts in banking fraud. We discuss how those scenarios can be adopted to identify user or expert requirements for appropriate explanations in his daily operations and to make decisions on reviewing fraudulent cases in banking. The generalizability of the scenarios for further adoption is validated through a systematic literature review in domains of XAI and visual analytics for fraud detection.

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Section: Related Workmentioning

confidence: 99%

“…In summary, it is observed in [38,39,41,42] proposals aiming for the integration of explanations in a fraud detection context. Within visual analytics literature, it is also observed studies aiming to support the decision-making of fraud experts through visualizations [21,45,46].…”

Section: Related Workmentioning

confidence: 99%

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Cirqueira

Nedbal

Helfert

et al. 2020

Lecture Notes in Computer Science

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“…In summary, prior studies on credit risk assessment models for financial institutions focus on improving imbalanced data or classification accuracy through multistage modeling and DL. Although these methods can somewhat boost accuracy, the following aspects need to be further addressed: low time responsiveness, transparency, interpretability, and analysis of behavior features (Laughlin, Sankaranarayanan, & El-Khatib, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

Credit Risk Models for Financial Fraud Detection

Xia

Zhang

2023

Journal of Database Management

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Outlier detection is currently applied in many fields, where existing research focuses on improving imbalanced data or enhancing classification accuracy. In the financial area, financial fraud detection puts higher demands on real-time and interpretability. This paper attempts to develop a credit risk model for financial fraud detection based on an extreme gradient boosting tree (XGBoost). SMOTE is adopted to deal with imbalanced data. AUC is the assessment indicator, and the running time is taken as the reference to compare with other frequently used classification algorithms. The results indicate that the method proposed by this paper performs better than others. At the same time, XGBoost can obtain a ranking of important features that impact the classification results when performing classification tasks, making the evaluation results of the model interpretable. The above shows that the model proposed in the paper is more practical in solving credit risk assessment problems. It has faster response times, reduced costs, and better interpretability.

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“…Previous studies employing XAI and EM for fraud detection explore the effects and performance of explanations in fraud expert's work. In [31], authors provide a service architecture for security experts with explanations, aiming to introduce more context for the outlier score given anomalous records of network flows. In [12], authors provide experts with Shapley Additive Explanations (SHAP) [34] for why particular warranty claims are marked as anomalies by a machine learning (ML) model.…”

Section: Theoretical Foundation and Related Workmentioning

confidence: 99%

Towards Design Principles for User-Centric Explainable AI in Fraud Detection

Cirqueira

Helfert

Bezbradica

2021

Lecture Notes in Computer Science

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Experts rely on fraud detection and decision support systems to analyze fraud cases, a growing problem in digital retailing and banking. With the advent of Artificial Intelligence (AI) for decision support, those experts face the black-box problem and lack trust in AI predictions for fraud. Such an issue has been tackled by employing Explainable AI (XAI) to provide experts with explained AI predictions through various explanation methods. However, fraud detection studies supported by XAI lack a user-centric perspective and discussion on how principles are deployed, both important requirements for experts to choose an appropriate explanation method. On the other hand, recent research in Information Systems (IS) and Human-Computer Interaction highlights the need for understanding user requirements to develop tailored design principles for decision support systems. In this research, we adopt a design science research methodology and IS theoretical lens to develop and evaluate design principles, which align fraud expert's tasks with explanation methods for Explainable AI decision support. We evaluate the utility of these principles using an information quality framework to interview experts in banking fraud, plus a simulation. The results show that the principles are an useful tool for designing decision support systems for fraud detection with embedded user-centric Explainable AI.

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A Service Architecture Using Machine Learning to Contextualize Anomaly Detection

Cited by 10 publications

References 42 publications

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Credit Risk Models for Financial Fraud Detection

Towards Design Principles for User-Centric Explainable AI in Fraud Detection

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