The FinCEN AI System: Finding Financial Crimes in a Large Database of Cash Transactions

Goldberg, Henry G.; Senator, Ted E.

doi:10.1007/978-3-662-03678-5_15

Cited by 9 publications

(7 citation statements)

References 2 publications

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“…One of the most elaborate money laundering detection systems is the U.S. Financial Crimes Enforcement Network AI system (FAIS) described in Senator et al (1995) and Goldberg and Senator (1998). This system allows users to follow trails of linked transactions.…”

Section: Money Launderingmentioning

confidence: 99%

Statistical Fraud Detection: A Review

Bolton¹,

Hand²

2002

Statist. Sci.

882

475

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Fraud is increasing dramatically with the expansion of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. Although prevention technologies are the best way to reduce fraud, fraudsters are adaptive and, given time, will usually find ways to circumvent such measures. Methodologies for the detection of fraud are essential if we are to catch fraudsters once fraud prevention has failed. Statistics and machine learning provide effective technologies for fraud detection and have been applied successfully to detect activities such as money laundering, e-commerce credit card fraud, telecommunications fraud and computer intrusion, to name but a few. We describe the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used.

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Section: Money Launderingmentioning

confidence: 99%

Statistical Fraud Detection: A Review

Bolton¹,

Hand²

2002

Statist. Sci.

882

475

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“…There are numerous software agent tools, which are widely used in security investigation context. For example, FinCEN is used to detect money laundering through the FinCEN Artificial Intelligence System (FIAS) . InfoGist is an information retrieval software agent tool that can search and aggregate a set of webpages on the basis of some defined keywords.…”

Section: Data Mining Techniquesmentioning

confidence: 99%

Big data analytics for security and criminal investigations

Pramanik

Lau

Yue

et al. 2017

WIREs Data Min & Knowl

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Applications of various data analytics technologies to security and criminal investigation during the past three decades have demonstrated the inception, growth, and maturation of criminal analytics. We first identify five cutting‐edge data mining technologies such as link analysis, intelligent agents, text mining, neural networks, and machine learning. Then, we explore their recent applications to the criminal analytics domain, and discuss the challenges arising from these innovative applications. We also extend our study to big data analytics which provides some state‐of‐the‐art technologies to reshape criminal investigations. In this paper, we review the recent literature, and examine the potentials of big data analytics for security intelligence under a criminal analytics framework. We examine some common data sources, analytics methods, and applications related to two important aspects of social network analysis namely, structural analysis and positional analysis that lay the foundation of criminal analytics. Another contribution of this paper is that we also advocate a novel criminal analytics methodology that is underpinned by big data analytics. We discuss the merits and challenges of applying big data analytics to the criminal analytics domain. Finally, we highlight the future research directions of big data analytics enhanced criminal investigations. WIREs Data Mining Knowl Discov 2017, 7:e1208. doi: 10.1002/widm.1208 This article is categorized under: Fundamental Concepts of Data and Knowledge > Data Concepts Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Technologies > Computer Architectures for Data Mining

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“…Questions that traditionally required extensive hands-on analysis, experts and time, can potentially be quickly answered from a firm's existing data. Goldberg and Senator (1998) …”

Section: Data Miningmentioning

confidence: 99%

Dealing with construction cost overruns using data mining

Ahiaga-Dagbui

Smith

2014

Construction Management and Economics

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One of the main aims of any construction client is to procure a project within the limits of a predefined budget. However, most construction projects routinely to overrun their cost estimates. Existing theories on construction cost overrun suggest a number of causes ranging from technical difficulties, optimism bias, managerial incompetence and strategic misrepresentation. However, much of the budgetary decision making process in the early stages of a project is carried out in an environment of high uncertainty with little available information for accurate estimation. Using nonparametric bootstrapping and ensemble modelling in artificial neural networks, final project cost forecasting models were developed with 1600 completed projects in this experimental research. This helped to extract information embedded in data on completed construction projects, in an attempt to address the problem of dearth of information in the early stages of a project. 92% of the 100 validation predictions were within ±10% of the actual final cost of the project whiles 77% were within ±5% of actual final cost. This indicates the model's ability to generalise satisfactorily when validated with new data. The models are being deployed within the operations of the industry partner involved in this research to help increase the reliability and accuracy of initial cost estimates.

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The FinCEN AI System: Finding Financial Crimes in a Large Database of Cash Transactions

Cited by 9 publications

References 2 publications

Statistical Fraud Detection: A Review

Statistical Fraud Detection: A Review

Big data analytics for security and criminal investigations

Dealing with construction cost overruns using data mining

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