scispace - formally typeset
Search or ask a question
Proceedings ArticleDOI

A Framework for Discovering Internal Financial Fraud Using Analytics

03 Jun 2011-pp 323-327
TL;DR: A framework called "Knowledge-driven Internal Fraud Detection (KDIFD)" is proposed for detecting internal financial frauds that considers both forensic auditor's tacit knowledge base and computer-based data analysis and mining techniques.
Abstract: In today's knowledge based society, financial fraud has become a common phenomenon. Moreover, the growth in knowledge discovery in databases and fraud audit has made the detection of internal financial fraud a major area of research. On the other hand, auditors find it difficult to apply a majority of techniques in the fraud auditing process and to integrate their domain knowledge in this process. In this Paper a framework called "Knowledge-driven Internal Fraud Detection (KDIFD)" is proposed for detecting internal financial frauds. The framework suggests a process-based approach that considers both forensic auditor's tacit knowledge base and computer-based data analysis and mining techniques. The proposed framework can help auditor in discovering internal financial fraud more efficiently.
Citations
More filters
01 Jan 2002

9,314 citations

Journal ArticleDOI
TL;DR: This paper proposes common database forensic investigation processes using a design science research approach that allows the reconciliation of the concepts and terminologies of all common database forensics investigation processes and facilitates the sharing of knowledge on database Forensic investigation among domain newcomers, users, and practitioners.
Abstract: Database forensics is a domain that uses database content and metadata to reveal malicious activities on database systems in an Internet of Things environment. Although the concept of database forensics has been around for a while, the investigation of cybercrime activities and cyber breaches in an Internet of Things environment would benefit from the development of a common investigative standard that unifies the knowledge in the domain. Therefore, this paper proposes common database forensic investigation processes using a design science research approach. The proposed process comprises four phases, namely: 1) identification; 2) artefact collection; 3) artefact analysis; and 4) the documentation and presentation process. It allows the reconciliation of the concepts and terminologies of all common database forensic investigation processes; hence, it facilitates the sharing of knowledge on database forensic investigation among domain newcomers, users, and practitioners.

37 citations

Journal ArticleDOI
01 Feb 2017-PLOS ONE
TL;DR: This work has analysed 60 models of DBF in an attempt to uncover how numerous DBF activities are really public even when the actions vary, and generates a unified abstract view ofDBF in the form of a metamodel.
Abstract: Database Forensics (DBF) is a widespread area of knowledge. It has many complex features and is well known amongst database investigators and practitioners. Several models and frameworks have been created specifically to allow knowledge-sharing and effective DBF activities. However, these are often narrow in focus and address specified database incident types. We have analysed 60 such models in an attempt to uncover how numerous DBF activities are really public even when the actions vary. We then generate a unified abstract view of DBF in the form of a metamodel. We identified, extracted, and proposed a common concept and reconciled concept definitions to propose a metamodel. We have applied a metamodelling process to guarantee that this metamodel is comprehensive and consistent.

36 citations

Journal Article
TL;DR: This paper provide a comprehensive survey and review of different techniques to detect the financial fraud detection used in various fraud like credit card fraud detection, online auction fraud, telecommunication Fraud detection, and computer intrusion detection.
Abstract: to levitate and rapid escalation of E-Commerce, cases of financial fraud allied with it are also intensifying and which results in trouncing of billions of dollars worldwide each year. Fraud detection involves scrutinizing the behavior of populations of users in order to ballpark figure, detect, or steer clear of objectionable behavior: Undesirable behavior is a extensive term including delinquency: swindle, infringement, and account evasion. Factually, swindle transactions are speckled with genuine transactions and simple pattern matching techniques are not often sufficient to detect those frauds accurately. In this survey we, will focuses on classifying fraudulent behaviors, identifying the major sources and characteristics of the data based on which fraud detection has been conducted. This paper provide a comprehensive survey and review of different techniques to detect the financial fraud detection used in various fraud like credit card fraud detection, online auction fraud, telecommunication fraud detection, and computer intrusion detection.

29 citations


Cites background from "A Framework for Discovering Interna..."

  • ...Knowledge Driven Internal Fraud Detection (KDIFD) framework is proposed for discovering internal financial fraud in paper [3]....

    [...]

Proceedings ArticleDOI
19 Sep 2012
TL;DR: An anti-money laundering model is proposed by combining digital forensics practices along with database tools and database analysis methodologies and admissible Suspicious Activity Reports (SARs) can be generated, based on evidence obtained from forensically analysing database financial logs in compliance with Know-Your-Customer policies for money laundering detection.
Abstract: Digital forensics is the science that identify, preserve, collect, validate, analyse, interpret, and report digital evidence that may be relevant in court to solve criminal investigations. Conversely, money launderingis a form of crime that is compromising the internal policies in financial institutions, which is investigated by analysing large amount of transactional financial data. However, the majority of financial institutions have adopted ineffective detection procedures and extensive reporting tasks to detect money laundering without incorporating digital forensic practices to handle evidence. Thus, in this article, we propose an anti-money laundering model by combining digital forensics practices along with database tools and database analysis methodologies. As consequence, admissible Suspicious Activity Reports (SARs) can be generated, based on evidence obtained from forensically analysing database financial logs in compliance with Know-Your-Customer policies for money laundering detection.

18 citations


Cites background or methods from "A Framework for Discovering Interna..."

  • ...In fact, understanding these policies can support the detection process by defining thresholds according to the organisational goals [22] to prevent money laundering....

    [...]

  • ...• Identify data quality issues that can affect the evidence due to incomplete and inaccurate data as well as inappropriate data granularity and wrong formats [22]....

    [...]

  • ...Although, the whole database should be examined to detect frauds [22], if the proper data sources have been identified, there is no need to examine the whole database, because the extractors can filter the information automatically and store it in customized database logs, ensuring the normal server’s function by planning and targeting the database activities [23]....

    [...]

  • ...Hence, if a statistical approach is used to generate ‘SARs’, an appropriate data analysis tool can be chosen to create and present an accurate and understandable report [22]....

    [...]

  • ...Also, non-accounting databases, like customer information, have to be identified along with non-electronic data sources which must be transferred to electronic formats [22]....

    [...]

References
More filters
Book
08 Sep 2000
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
Abstract: The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

23,600 citations

01 Jan 2002

9,314 citations


"A Framework for Discovering Interna..." refers background in this paper

  • ...Data mining is the process of discovering hidden facts, or ‘red flags’, trends, patterns and relationships from multiple databases and has been used in fraud auditing process [2, 3] ....

    [...]

01 Jan 2006
TL;DR: There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99].
Abstract: The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

2,591 citations

Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

587 citations


"A Framework for Discovering Interna..." refers background in this paper

  • ...Data mining is the process of discovering hidden facts, or ‘red flags’, trends, patterns and relationships from multiple databases and has been used in fraud auditing process [2, 3] ....

    [...]