Financial fraud detection plays a crucial role in the stability of institutions and the economy at large. Data mining methods have been used to detect/flag cases of fraud due to a large amount of data and possible concept drift. In the financial statement fraud detection domain, instances containing missing values are usually discarded from experiments and this may lead to a loss of crucial inf...

Enterprise systems, real time recording and real time reporting pose new and significant challenges to the accounting and auditing professions. This includes developing methods and tools for continuous assurance and fraud detection. In this paper we propose a methodology for continuous fraud detection that exploits security audit logs, changes in master records and accounting audit trails in en...

Adaptive Benford’s Law [1] is a digital analysis technique that specifies the probabilistic distribution of digits for many commonly occurring phenomena, even for incomplete data records. We combine this digital analysis technique with a reinforcement learning technique to create a new fraud discovery approach. When applied to records of naturally occurring phenomena, our adaptive fraud detecti...

We estimate what percentage of firms engage in fraud and the economic cost of fraud. Our estimates are based on detected frauds, and frauds that we infer are started but are not caught. To identify the ‘iceberg’ of undetected fraud we take advantage of an exogenous shock to the incentives for fraud detection: Arthur Andersen’s demise, which forces companies to change auditors. By assuming that ...

The retail sector often does not possess sufficient knowledge about potential or actual frauds. This requires the retail sector to employ an anomaly detection approach to fraud detection. To detect anomalies in retail transactions, the fraud detection system introduced in this work implements various salient features of the human immune system. This novel artificial immune system, called CIFD (...

Article history: Received 8 June 2012 Received in revised form 7 January 2015 Accepted 11 January 2015 Available online 27 January 2015 Analysis of data to detect transaction anomalies is an important fraud detection procedure. Interactive data visualization tools that allow the investigator to change the representation of data from text to graphics and filter out subsets of transactions for fu...

Recent decades have seen a gigantic expansion in the use of credit cards as a true transactional medium. Data mining is rising as one of the chief features of many homeland security\ initiatives. Often, it is used as a means for detecting fraud, assessing risk, as well as product retailing. Data mining is becoming increasingly common in both the private as well as public sectors. Data mining in...

Telecommunications fraud not only burdens telecom provider’s accountings but burdens individual users as well. The latter are particularly affected in the case of superimposed fraud where the fraudster uses a legitimate user’s account in parallel with the user. These cases are usually identified after user complaints for excess billing. However, inside the network of a large firm or organizatio...

This paper aimed to build unified pattern per customer not only represent normal behavior but also Fraud pattern that’s represented previously and confirmed as fraud transactions that’s facilitate studding fraudsters behavior. An enhancement for the proposed algorithm of Fraud Miner has been proposed. This enhancement involves introducing LINGO clustering Data mining algorithm by replacing Apri...

At time of crisis, when fraud permanently frightens the basis of modern societies, the existence of effective tools to prevent it, or just to identify it in time, is critical. However, the detection of fraud is naturally impaired (among other issues) by the difficulty on labelling data, due to the cost of identifying and attest fraud. Moreover, the inability to incorporate domain knowledge in t...