Identification of process-based fraud patterns in credit application

Huda, Solichul; Ahmad, Tohari; Sarno, Riyanarto; Santoso, Heru Agus

doi:10.1109/icoict.2014.6914045

Cited by 30 publications

(25 citation statements)

References 6 publications

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“…The replay fitness was used to measure accuracy of the model with activity in the event logs done by calculating the value of fitness [11] [12].…”

Section: Quality Of Process Model Quality Dimensions Of Fitnessmentioning

confidence: 99%

A Combination of the Evolutionary Tree Miner and Simulated Annealing

Nurlaili

Sarno

2017

EECSI

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In recent years, process mining is important to discover process model from event logs; however the existing methods have not achieved good in overall fitness. In this context, this paper proposes a combination of the Evolutionary Tree Miner (ETM) and Simulated Annealing (SA). The ETM aims to reduce randomness of population so that it can improved the quality of individuals. SA aims to increase overall fitness in the population. The results of the proposed method which was compared to other approaches show that the proposes method had better in overall fitness and better quality of individuals.

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“…The replay fitness was used to measure accuracy of the model with activity in the event logs done by calculating the value of fitness [11] [12].…”

Section: Quality Of Process Model Quality Dimensions Of Fitnessmentioning

confidence: 99%

A Combination of the Evolutionary Tree Miner and Simulated Annealing

Nurlaili

Sarno

2017

EECSI

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“…Detection is performed using data mining (i.e. association rule learning) and a combination of data mining and process mining (hybrid method), the results of which are then analyzed based on the respective business processes to identify SOP deviation [11]- [13].…”

Section: Related Workmentioning

confidence: 99%

“…Process-based fraud (PBF) is a form of fraud that can be identified by processes that deviate from the SOP [13]. Detecting PBF in business processes can be done from three different angles.…”

Section: Process-based Fraud Detectionmentioning

confidence: 99%

“…For example, conformance checking can be used to compare business processes with their SOP. Furthermore, this method is able to detect the occurrence of event skipping, which is identified as suspicious [13] in addition to its capability of controlling and analyzing the flow of a business process. Using this method, the sequence of processes in a business can be analyzed.…”

Section: Process-based Fraud Detectionmentioning

confidence: 99%

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Fuzzy MADM Approach for Rating of Process-Based Fraud

2015

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Process-Based Fraud (PBF) is fraud enabled by process deviations that occur in business processes. Several studies have proposed PBF detection methods; however, false decisions are still often made because of cases with low deviation. Low deviation is caused by ambiguity in determining fraud attribute values and low frequency of occurrence. This paper proposes a method of detecting PBF with low deviation in order to correctly detect fraudulent cases. Firstly, the fraudulence attributes are established, then a fuzzy approach is utilized to weigh the importance of the fraud attributes. Further, multi-attribute decision making (MADM) is employed to obtain a PBF rating according to attribute values and attribute importance weights. Finally, a decision is made whether the deviation is fraudulent or not, based on the PBF rating. Experimental validation showed that the accuracy and false discovery rate of the method were 0.98 and 0.17, respectively.

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“…Nowadays, process modeling has become a concern. A process model can be used for analyzing executable processes in the information systems and understanding the behavior of executor in implementing the processes [1]- [3]. All research about process discovery, e.g.…”

Section: Introductionmentioning

confidence: 99%

CHMM for Discovering Intentional Process Model From Event Logs by Considering Sequence of Activities

Sungkono¹,

Sarno²

2017

EECSI

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An intentional process model is known to analyze processes deeply and provide recommendations for the upcoming processes. Nevertheless, the discovery of intentions is a difficult task because the intentions are not recorded in the event log, but they encourage the executable activities in the event log. Map Miner is the latest algorithm to depict the intentional process model. A disadvantage of this algorithm is the inability to determine strategies that contain same activities with the different sequence with other strategies. This disadvantage leads failure on the intentional process model. This research proposes an algorithm for discovering an intentional process model by considering the sequence of activities and CHMM (Coupled Hidden Markov Model). The probabilities and states of CHMM are utilized for the formation of the intentional process model. The experiment shows that the proposed algorithm with considering the sequence of activities gets an appropriate intentional process model. It also demonstrates that an obtained intentional process model using proposed algorithm gets the better validity than an intentional process model using Map Miner Method.

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Identification of process-based fraud patterns in credit application

Cited by 30 publications

References 6 publications

A Combination of the Evolutionary Tree Miner and Simulated Annealing

A Combination of the Evolutionary Tree Miner and Simulated Annealing

Fuzzy MADM Approach for Rating of Process-Based Fraud

CHMM for Discovering Intentional Process Model From Event Logs by Considering Sequence of Activities

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