Money Laundering Analytics Based on Contextual Analysis. Application of Problem Solving Ontologies in Financial Fraud Identification and Recognition

Chmielewski, Mariusz; Stąpor, Piotr

doi:10.1007/978-3-319-46583-8_3

Cited by 5 publications

(7 citation statements)

References 3 publications

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“…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. [22,36,43,44,55,68,71,72,80,88,89,92,93,95,96,97,98,101,102,103,104,105,106,107] 24 Yes…”

Section: Usage Of Scenarios For Requirements Elicitation For Explanatmentioning

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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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: Usage Of Scenarios For Requirements Elicitation For Explanatmentioning

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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“…The system loads the necessary definitions and data and then maps the transaction records to the established instances (Chmielewski & Stąpor, 2016). In the preprocessing step, the extraction of resources to be analyzed is performed (Baek et al, 2019).…”

Section: General Concept According To the Selected Articlesmentioning

confidence: 99%

Anti‐money laundering and financial fraud detection: A systematic literature review

Goecks

Korzenowski

Neto

et al. 2022

Intell Sys Acc Fin Mgmt

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Money laundering has affected the global economy for many years, and there are several methods of solving it presented in the literature. However, when tackling money laundering and financial fraud together there are few methods for solving them. Thus, this study aims to identify methods for anti‐money laundering (AML) and financial fraud detection (FFD). A systematic literature review was performed for analysis and research of the methods used, utilizing the SCOPUS and Web of Science databases. Of the 48 articles that aligned with the research theme, 20 used quantitative methods for AML and FFD solution, 13 were literature reviews, 7 used qualitative methods, and 8 used mixed methods. This study contributes by presenting a systematic literature review that fills two research gaps: lack of studies on AML and FFD, and the methods used to solve them. This will assist researchers in identifying gaps and related research.

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“…Of course, a pair of vertices from different components can in reality be associated, but a probability that such semantic linkage exists is considerably negligible given problem's size and complexity. [7] Because of resulting multigraphs' magnitude, it might be both difficult and time consuming for user to consume the entire output. It is natural to expect that within such large structure the importance levels of some discoveries prevail over others.…”

Section: Starsoft Approachmentioning

confidence: 99%

“…The tool allowed us to fuse values acquired from hierarchical perspective with structural data. More information about the dedicated application -IAFEC Semantic Association Analyzer tool can be found in [7].…”

Section: Evaluation Environmentmentioning

confidence: 99%

“…Research projects FF POIROT [1], DOGMA [2] discuss domain models as methods for information retrieval. An extended approach is presented in this paper, which concentrates on application of ontology models, structural analysis and quantitative approach [3] [6] [7] to develop and characterise inference schemes on which a set of tools supporting the analyst has been developed [3]…”

Section: Introduction and Main Conceptmentioning

confidence: 99%

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Hidden information retrieval and evaluation method and tools utilising ontology reasoning applied for financial fraud analysis.

Chmielewski

Stąpor

2018

MATEC Web Conf.

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The paper summarizes a semantic association evaluation and reasoning method, utilising domain and problem solving ontologies. The method combines algorithms for data aggregation and logic reasoning utilising concrete financial data. As an outcome method supports suspicious behaviour recognition of money loundering schemes. These scenarios and schemes are implemented by the analysts using ontology-based constructs. Provided tools cover all stages of data processing starting from structural data extraction and migration, aggregation to reasoning using logic (DL and FOL) constructs. Advances in automatic reasoning and the availability of semantic processing tools encourage analysts to extend existing link analysis methods towards contextual data processing. To demonstrate presented method, a proof of concept environment IAFEC Ontology Toolkit has been described. It delivers initial financial fraud identification schemes (rules) based on set of problem solving ontologies. The novelty in such approach comes from incorporating heterogeneous types of data, which usually are processed by graph methods. The semantic tool, extend capabilities of graph-based (homogeneous) approach by delivering context-aware indirect association identification, and inference path explanation and inspection capabilities. Presented material describes the method and analytical algorithms, which demonstrate description logic reasoning and graph-based semantic association identification and ranking. Developed method has been implemented, as a Protégé OWL 5.0 environment extension, supplemented with web-services delivering distributed data processing, aggregation (not available in ontology languages). The environment provides declarative processing capabilities enabling analysts to design configurable processing flow, and new financial fraud identification schemes.

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Money Laundering Analytics Based on Contextual Analysis. Application of Problem Solving Ontologies in Financial Fraud Identification and Recognition

Cited by 5 publications

References 3 publications

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Scenario-Based Requirements Elicitation for User-Centric Explainable AI

Anti‐money laundering and financial fraud detection: A systematic literature review

Hidden information retrieval and evaluation method and tools utilising ontology reasoning applied for financial fraud analysis.

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