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"windows-1252", "filename": "stamp.html", "md5": null, "mtime": 2147483647, "tags": [], "relations": {}, "dateAdded": "2010-01-28T01:57:44Z", "dateModified": "2010-01-28T01:57:44Z" } }, { "key": "5GZSFUQ5", "version": 1, "library": { "type": "group", "id": 10208, "name": "Financial Statement Fraud", "links": { "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/10208/items/5GZSFUQ5", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud/items/5GZSFUQ5", "type": "text/html" } }, "meta": { "createdByUser": { "id": 113960, "username": "wuerges", "name": "Artur Filipe Ewald Wuerges", "links": { "alternate": { "href": "https://www.zotero.org/wuerges", "type": "text/html" } } }, "creatorSummary": "Gaganis", "parsedDate": "2009", "numChildren": 1 }, "data": { "key": "5GZSFUQ5", "version": 1, "itemType": "journalArticle", "title": "Classification techniques for the identification of falsified financial statements: a comparative analysis", "creators": [ { "creatorType": "author", "firstName": "Chrysovalantis", "lastName": "Gaganis" } ], "abstractNote": "", "publicationTitle": "Intelligent Systems in Accounting, Finance & Management", "publisher": "", "place": "", "date": "07/2009", "volume": "16", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "207-229", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Intell. Syst. Acc. Fin. Mgmt.", "DOI": "10.1002/isaf.303", "citationKey": "", "url": "http://doi.wiley.com/10.1002/isaf.303", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1055615X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "T2XCBIC8" ], "relations": {}, "dateAdded": "2010-01-28T01:57:44Z", "dateModified": "2010-01-28T01:57:44Z" } }, { "key": "B3DJE686", "version": 1, "library": { "type": "group", "id": 10208, "name": "Financial Statement Fraud", "links": { "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/10208/items/B3DJE686", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud/items/B3DJE686", "type": "text/html" } }, "meta": { "createdByUser": { "id": 113960, "username": "wuerges", "name": "Artur Filipe Ewald Wuerges", "links": { "alternate": { "href": "https://www.zotero.org/wuerges", "type": "text/html" } } }, "creatorSummary": "Qian Liu et al.", "parsedDate": "2009", "numChildren": 1 }, "data": { "key": "B3DJE686", "version": 1, "itemType": "conferencePaper", "title": "A subjective and objective integrated method for fraud detection in financial systems", "creators": [ { "creatorType": "author", "firstName": "", "lastName": "Qian Liu" }, { "creatorType": "author", "firstName": "", "lastName": "Tong Li" }, { "creatorType": "author", "firstName": "", "lastName": "Wei Xu" } ], "abstractNote": "Financial statement fraud (FSF) has cost market participants, including investors, creditors, pensioners, and employees, more than $500 billion during decades. Especially in recent years, with the worldwide use of financial systems in companies, governments and universities, fraud in financial systems can be in terms of computer, network, customer or even staff and all will remain keys in assessing financial system risk. Traditional methods such as auditing or statistics models used to detect fraud in FSF can't effectively select the intrinsic features in financial systems. This paper focuses on identity theft fraud in financial systems and proposes an integrated framework including subjective methods and objective models for fraud detection in financial systems. The subjective and objective integrated framework employs AHP and rough set (RS) to analyze the fraud scenarios, select the intrinsic features, detect the abnormities and alarm. The proposed framework used to detect identity theft fraud can be also used to detect and prevent other types of fraud in financial systems.", "proceedingsTitle": "Machine Learning and Cybernetics, 2009 International Conference on", "conferenceName": "Machine Learning and Cybernetics, 2009 International Conference on", "publisher": "", "place": "", "date": "2009", "eventPlace": "", "volume": "3", "issue": "", "numberOfVolumes": "", "pages": "1339-1345", "series": "", "seriesNumber": "", "DOI": "", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "IEEE Xplore", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "AHP", "type": 1 }, { "tag": "FSF", "type": 1 }, { "tag": "Financial system", "type": 1 }, { "tag": "Fraud detection", "type": 1 }, { "tag": "abnormities detection", "type": 1 }, { "tag": "alarm detection", "type": 1 }, { "tag": "analytic hierarchy process", "type": 1 }, { "tag": "auditing models", "type": 1 }, { "tag": "decision making", "type": 1 }, { "tag": "financial data processing", "type": 1 }, { "tag": "financial statement fraud", "type": 1 }, { "tag": "financial system risk", "type": 1 }, { "tag": "identity theft fraud", "type": 1 }, { "tag": "objective integrated method", "type": 1 }, { "tag": "rough set", "type": 1 }, { "tag": "rough set theory", "type": 1 }, { "tag": "security of data", "type": 1 }, { "tag": "statistics models", "type": 1 }, { "tag": "subjective integrated method", "type": 1 } ], "collections": [ "T2XCBIC8" ], "relations": {}, "dateAdded": "2010-01-28T01:57:44Z", "dateModified": "2010-01-28T01:57:44Z" } }, { "key": "W8T847K2", "version": 1, "library": { "type": "group", "id": 10208, "name": "Financial Statement Fraud", "links": { "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/10208/items/W8T847K2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud/items/W8T847K2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 113960, "username": "wuerges", "name": "Artur Filipe Ewald Wuerges", "links": { "alternate": { "href": "https://www.zotero.org/wuerges", "type": "text/html" } } }, "creatorSummary": "Ogut et al.", "parsedDate": "2009", "numChildren": 1 }, "data": { "key": "W8T847K2", "version": 1, "itemType": "journalArticle", "title": "Prediction of financial information manipulation by using support vector machine and probabilistic neural network", "creators": [ { "creatorType": "author", "firstName": "H.", "lastName": "Ogut" }, { "creatorType": "author", "firstName": "R.", "lastName": "Aktaş" }, { "creatorType": "author", "firstName": "A.", "lastName": "Alp" }, { "creatorType": "author", "firstName": "M. M", "lastName": "Doğanay" } ], "abstractNote": "", "publicationTitle": "Expert Systems With Applications", "publisher": "", "place": "", "date": "2009", "volume": "36", "issue": "3P1", "section": "", "partNumber": "", "partTitle": "", "pages": "5419–5423", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "T2XCBIC8" ], "relations": {}, "dateAdded": "2010-01-28T01:57:44Z", "dateModified": "2010-01-28T01:57:44Z" } }, { "key": "JHAIN4ID", "version": 1, "library": { "type": "group", "id": 10208, "name": "Financial Statement Fraud", "links": { "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/10208/items/JHAIN4ID", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud/items/JHAIN4ID", "type": "text/html" } }, "meta": { "createdByUser": { "id": 113960, "username": "wuerges", "name": "Artur Filipe Ewald Wuerges", "links": { "alternate": { "href": "https://www.zotero.org/wuerges", "type": "text/html" } } }, "creatorSummary": "Kirkos et al.", "parsedDate": "2007-05", "numChildren": 1 }, "data": { "key": "JHAIN4ID", "version": 1, "itemType": "journalArticle", "title": "Data Mining techniques for the detection of fraudulent financial statements", "creators": [ { "creatorType": "author", "firstName": "Efstathios", "lastName": "Kirkos" }, { "creatorType": "author", "firstName": "Charalambos", "lastName": "Spathis" }, { "creatorType": "author", "firstName": "Yannis", "lastName": "Manolopoulos" } ], "abstractNote": "This paper explores the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS. In accomplishing the task of management fraud detection, auditors could be facilitated in their work by using Data Mining techniques. This study investigates the usefulness of Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. The input vector is composed of ratios derived from financial statements. The three models are compared in terms of their performances.", "publicationTitle": "Expert Systems with Applications", "publisher": "", "place": "", "date": "Maio 2007", "volume": "32", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "995-1003", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1016/j.eswa.2006.02.016", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/B6V03-4JD0XX9-4/2/ec371f528f59c5833d091fca237a41fd", "accessDate": "2010-01-20T19:44:01Z", "PMID": "", "PMCID": "", "ISSN": "0957-4174", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Auditing", "type": 1 }, { "tag": "Data Mining", "type": 1 }, { "tag": "Fraudulent financial statements", "type": 1 }, { "tag": "Greece", "type": 1 }, { "tag": "Management fraud", "type": 1 } ], "collections": [ "T2XCBIC8" ], "relations": {}, "dateAdded": "2010-01-28T01:57:44Z", "dateModified": "2010-01-28T01:57:44Z" } }, { "key": "PR374P76", "version": 1, "library": { "type": "group", "id": 10208, "name": "Financial Statement Fraud", "links": { "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/10208/items/PR374P76", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud/items/PR374P76", "type": "text/html" } }, "meta": { "createdByUser": { "id": 113960, "username": "wuerges", "name": "Artur Filipe Ewald Wuerges", "links": { "alternate": { "href": "https://www.zotero.org/wuerges", "type": "text/html" } } }, "creatorSummary": "Lin et al.", "parsedDate": "2003", "numChildren": 3 }, "data": { "key": "PR374P76", "version": 1, "itemType": "journalArticle", "title": "A fuzzy neural network for assessing the risk of fraudulent financial reporting", "creators": [ { "creatorType": "author", "firstName": "J. W", "lastName": "Lin" }, { "creatorType": "author", "firstName": "M. I", "lastName": "Hwang" }, { "creatorType": "author", "firstName": "J. D", "lastName": "Becker" } ], "abstractNote": "While financial reporting fraud has become more prevalent and costly in recent years, fraud detection has been badly lagging. Several recent studies have examined the feasibility of various computer techniques in business and industrial applications. The purpose of this study is to evaluate the utility of an integrated fuzzy neural network (FNN) for fraud detection. The FNN developed in this research outperformed most statistical models and artificial neural networks (ANN) reported in prior studies. Its performance also compared favorably with a baseline Logit model, especially in the prediction of fraud cases.", "publicationTitle": "Managerial Auditing Journal", "publisher": "", "place": "", "date": "2003", "volume": "657", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "665", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Google Scholar", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "T2XCBIC8" ], "relations": {}, "dateAdded": "2010-01-28T01:57:44Z", "dateModified": "2010-01-28T01:57:44Z" } }, { "key": "4R9TTU7T", "version": 1, "library": { "type": "group", "id": 10208, "name": "Financial Statement Fraud", "links": { "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/10208/items/4R9TTU7T", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud/items/4R9TTU7T", "type": "text/html" } }, "meta": { "createdByUser": { "id": 113960, "username": "wuerges", "name": "Artur Filipe Ewald Wuerges", "links": { "alternate": { "href": "https://www.zotero.org/wuerges", "type": "text/html" } } }, "creatorSummary": "Fanning and Cogger", "parsedDate": "1998-03", "numChildren": 2 }, "data": { "key": "4R9TTU7T", "version": 1, "itemType": "journalArticle", "title": "Neural network detection of management fraud using published financial data", "creators": [ { "creatorType": "author", "firstName": "Kurt", "lastName": "Fanning" }, { "creatorType": "author", "firstName": "Kenneth O.", "lastName": "Cogger" } ], "abstractNote": "This paper uses a Artificial Neural Network (AutoNet) to develop a model for detecting management fraud. The study offers an in-depth examination of important publicly available predictors of fraudulent financial statements. We find a model with a high probability of detecting fraudulent financial statements on one sample. The study reinforces the validity and efficiency of AutoNet as a research tool and provides additional empirical evidence regarding the merits of suggested red flags for fraudulent financial statements. © 1998 John Wiley & Sons, Ltd.", "publicationTitle": "International Journal of Intelligent Systems in Accounting, Finance & Management", "publisher": "", "place": "", "date": "March 1998", "volume": "7", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "21-41", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1002/(SICI)1099-1174(199803)7:1<21::AID-ISAF138>3.0.CO;2-K", "citationKey": "", "url": "http://www.ingentaconnect.com/content/jws/isaf/1998/00000007/00000001/art00138", "accessDate": "2010-01-21T14:25:19Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IngentaConnect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "1, *", "type": 1 }, { "tag": "2", "type": 1 }, { "tag": "Artificial Intelligence", "type": 1 }, { "tag": "Management fraud", "type": 1 }, { "tag": "Neural Network", "type": 1 }, { "tag": "Red Flags", "type": 1 } ], "collections": [ "T2XCBIC8" ], "relations": {}, "dateAdded": "2010-01-28T01:57:44Z", "dateModified": "2010-01-28T01:57:44Z" } }, { "key": "9I5WTKMV", "version": 1, "library": { "type": "group", "id": 10208, "name": "Financial Statement Fraud", "links": { "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/10208/items/9I5WTKMV", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud/items/9I5WTKMV", "type": "text/html" } }, "meta": { "createdByUser": { "id": 113960, "username": "wuerges", "name": "Artur Filipe Ewald Wuerges", "links": { "alternate": { "href": "https://www.zotero.org/wuerges", "type": "text/html" } } }, "creatorSummary": "Deshmukh and Talluru", "parsedDate": "1997", "numChildren": 1 }, "data": { "key": "9I5WTKMV", "version": 1, "itemType": "conferencePaper", "title": "A rule based fuzzy reasoning system for assessing the risk of management fraud", "creators": [ { "creatorType": "author", "firstName": "A.", "lastName": "Deshmukh" }, { "creatorType": "author", "firstName": "T.L.N.", "lastName": "Talluru" } ], "abstractNote": "Statement on Auditing Standards (SAS) No. 53 requires that the\naudit be designed to provide a reasonable assurance of detecting\nmanagement fraud. Touche Ross (1974), Coopers and Lybrand (1977), and\nPrice Waterhouse (1985) released a list of red flags based on business\nand economic factors that can be used as a set of warning indicators.\nThe purpose of this paper is to demonstrate the use of a rule based\nfuzzy reasoning system to assess the risk of management fraud. Based on\nthe Bell et al. (1993) study we identified statistically significant red\nflags for each assessing the risk of management fraud. Using the\nstatistical significance of each red flag and the theoretical model we\ndeveloped membership functions and fuzzy rules. The rule based system\nwas implemented in XpertRule. Then the fuzzy reasoning system tested\nusing the same fraud data as in Bell et al. and the results obtained\nwere similar to statistical models. The fuzzy reasoning system can be\nused as an intuitive decision aid in assessing the risk of management\nfraud and in structured decision making", "proceedingsTitle": "Systems, Man, and Cybernetics, 1997. 'Computational Cybernetics and Simulation'., 1997 IEEE International Conference on", "conferenceName": "Systems, Man, and Cybernetics, 1997. 'Computational Cybernetics and Simulation'., 1997 IEEE International Conference on", "publisher": "", "place": "", "date": "1997", "eventPlace": "", "volume": "1", "issue": "", "numberOfVolumes": "", "pages": "669-673 vol.1", "series": "", "seriesNumber": "", "DOI": "10.1109/ICSMC.1997.625830", "ISBN": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "IEEE Xplore", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Auditing", "type": 1 }, { "tag": "Fraud", "type": 1 }, { "tag": "Management fraud", "type": 1 }, { "tag": "XpertRule", "type": 1 }, { "tag": "accounts data processing", "type": 1 }, { "tag": "decision making", "type": 1 }, { "tag": "economic factors", "type": 1 }, { "tag": "financial data processing", "type": 1 }, { "tag": "fuzzy reasoning system", "type": 1 }, { "tag": "fuzzy set theory", "type": 1 }, { "tag": "fuzzy systems", "type": 1 }, { "tag": "knowledge based systems", "type": 1 }, { "tag": "membership functions", "type": 1 }, { "tag": "risk assessment", "type": 1 }, { "tag": "rule based system", "type": 1 }, { "tag": "statistical model", "type": 1 } ], "collections": [ "T2XCBIC8" ], "relations": {}, "dateAdded": "2010-01-28T01:57:44Z", "dateModified": "2010-01-28T01:57:44Z" } }, { "key": "9STT9G8B", "version": 1, "library": { "type": "group", "id": 10208, "name": "Financial Statement Fraud", "links": { "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/10208/items/9STT9G8B", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/financial_statement_fraud/items/9STT9G8B", "type": "text/html" } }, "meta": { "createdByUser": { "id": 113960, "username": "wuerges", "name": "Artur Filipe Ewald Wuerges", "links": { "alternate": { "href": "https://www.zotero.org/wuerges", "type": "text/html" } } }, "creatorSummary": "Fanning et al.", "parsedDate": "1995", "numChildren": 1 }, "data": { "key": "9STT9G8B", "version": 1, "itemType": "conferencePaper", "title": "Detection of management fraud: a neural network approach", "creators": [ { "creatorType": "author", "firstName": "K.", "lastName": "Fanning" }, { "creatorType": "author", "firstName": "K.O.", "lastName": "Cogger" }, { "creatorType": "author", "firstName": "R.", "lastName": "Srivastava" } ], "abstractNote": "The detection of management fraud is an important issue facing the\nauditing profession. A major contributor to this issue is the Loebbecke\nand Willingham (1989) conceptual model for the detection of management\nfraud. A cascaded Logit approach using the Loebbecke and Willingham\nmodel was developed (Bell et al., 1993). The present study offers an\nalternative approach using artificial neural networks (ANNs). This paper\ndevelops a successful discriminator of management fraud using both the\ngeneralized adaptive neural network architectures (GANNA) and the\nadaptive logic network (ALN) approaches to designing neural networks.\nThe discriminant functions can distinguish between fraudulent and\nnon-fraudulent companies with superior accuracy to the cascaded Logit\nresults", "proceedingsTitle": "Artificial Intelligence for Applications, 1995. Proceedings., 11th Conference on", "conferenceName": "Artificial Intelligence for Applications, 1995. 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