Predicting deadline transgressions using event logs

Effective risk management is crucial for any organisation. One of its key steps is risk identification, but few tools exist to support this process. Here we present a method for the automatic discovery of a particular type of process-related risk, the danger of deadline transgressions or overruns, based on the analysis of event logs. We define a set of time-related process risk indicators, i.e., patterns observable in event logs that highlight the likelihood of an overrun, and then show how instances of these patterns can be identified automatically using statistical principles. To demonstrate its feasibility, the approach has been implemented as a plug-in module to the process mining framework ProM and tested using an event log from a Dutch financial institution

Business Process Anomali Detection using Multi-Level Class Association Rule Learning

Recently, Business Process Management System (BPMS) is widely used by companies in order to manage their business process. The company’s business process has a possibility to have changes which can cause some variations of business process. These variations might be contain some anomalies. Any anomalies that can make some losses for the company can be regarded as a fraud. There were some research have done to detect anomalies in business process. But, there is some issues that still need improvement especially on the accuracy. This paper proposed Multi-Level Class Association Rule Learning method (ML-CARL) to detect business process anomalies accurately. This method is supported by the process mining method which is used to analyze the anomalies in process. From the experiment, ML-CARL method can detect anomalies with an accuracy of 0.99 and better than ARL method in previous research. It can be concluded that ML-CARL method can increase the accuracy of business process anomaly detection

Intelligent Financial Fraud Detection Practices: An Investigation

Financial fraud is an issue with far reaching consequences in the finance industry, government, corporate sectors, and for ordinary consumers. Increasing dependence on new technologies such as cloud and mobile computing in recent years has compounded the problem. Traditional methods of detection involve extensive use of auditing, where a trained individual manually observes reports or transactions in an attempt to discover fraudulent behaviour. This method is not only time consuming, expensive and inaccurate, but in the age of big data it is also impractical. Not surprisingly, financial institutions have turned to automated processes using statistical and computational methods. This paper presents a comprehensive investigation on financial fraud detection practices using such data mining methods, with a particular focus on computational intelligence-based techniques. Classification of the practices based on key aspects such as detection algorithm used, fraud type investigated, and success rate have been covered. Issues and challenges associated with the current practices and potential future direction of research have also been identified.Comment: Proceedings of the 10th International Conference on Security and Privacy in Communication Networks (SecureComm 2014

Fraud detection using Process mining and analytical hierarchy process with verification rules on ERP business process

Today many corporate runs their business process by applying Enterprise Resource Planning (ERP). A corporate which has a well-defined business process will run its business process effectively and efficiently. In practice, the executed business process is not always complied with Standard Operational Procedure (SOP). The deviations of business process can be identified by analysing event logs from the business process activities using process mining method. The various kinds of deviation are skip activity, wrong throughput time, wrong actor, wrong duty, and wrong decision. But not all deviations are fraud. Hence, Analytical Hierarchy Process and verification rules are employed to analyse the deviations to determine the fraud status. The proposed method successfully detected frauds conducted in business processes of bank credit application and procurement process. The proposed method achieved up to 95% of accuracy

The role of Louvain-coloring clustering in the detection of fraud transactions

Clustering is a technique in data mining capable of grouping very large amounts of data to gain new knowledge based on unsupervised learning. Clustering is capable of grouping various types of data and fields. The process that requires this technique is in the business sector, especially banking. In the transaction business process in banking, fraud is often encountered in transactions. This raises interest in clustering data fraud in transactions. An algorithm is needed in the cluster, namely Louvain’s algorithm. Louvain’s algorithm is capable of clustering in large numbers, which represent them in a graph. So, the Louvain algorithm is optimized with colored graphs to facilitate research continuity in labeling. In this study, 33,491 non-fraud data were grouped, and 241 fraud transaction data were carried out. However, Louvain’s algorithm shows that clustering increases the amount of data fraud of 90% by accurate

Fuzzy MADM Approach for Rating of Process-Based Fraud

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 0.92 and 0.33, respectively

IDENTIFIKASI PROCESS-BASEDFRAUD DALAM APLIKASI TABUNGAN MENGGUNAKAN LEVEL EVENT LOGS

Fraud dalam aplikasi tabungan sebagian disebabkan oleh proses yang melanggarStandard Operating Procedure (SOP); fraud tersebut dikenal dengan istilah ProcessbasedFraud (PBF). Beberapa metode deteksi fraud sebelumnya tidak bisa mendeteksifraud dalam aplikasi tabungan, paper ini mengusulkan level event logs untukmengidentifikasi informasi tindakan pertugas (originator) yang menjalankan proses.Pertama, mengidentifikasi rangkaian tindakan/perilaku originator, dan merancanglevel event logs. Selanjutnya, berdasarkan perilaku originator, dihitung attribute value,bobot penting atribut dan bobot fraud. Hasil uji coba dari data testing menunjukkanbahwa metode level event logs ini dapat mengidentifikasi pelanggaran SOP dalamaplikasi tabungan yang memiliki SMS banking dengan akurasi yang lebih baik 0.01dibanding metode sebelumnya

Penentuan Attribute Value Untuk Menentukan Bobot Fraud Dalam Transaksi Online

Fraud atau penipuan sering terjadi dalam transaksi online. Beberapa penelitian sebelumnya telah mengusulkan metode deteksi fraud dalam transaksi online. Namun dalam penentuan beberapa attribute value ditentukan oleh pakar secara subyektif; paper ini mengusulkan metode untuk menentukan attribute value tersebut. Atribut fraud dalam transaksi online terdiri enam atribut yaitu throughput time, wrong pattern, skip, same location, relationship dan quantity. Tiga atribut ditentukan menggunakan metode yang diusulkan penelitian sebelumnya, sedangkan tiga atribut berikutnya ditentukan secara subyektif oleh pakar. Paper ini mengusulkan metode pembobotan attribute value, sehingga semua atribut fraud ditentukan secara komputasi. Dalam pembobotan attribute value, pertama, menganalisis pelanggaran transaksi data training terhadap Standard Operating Procedure (SOP). Selanjutnya pakar menentukan pelanggaran yang terjadi merupakan fraud atau tidak. Kemudian dihitung probabilitas masing-masing atribut tersebut terhadap fraud yang terjadi. Lalu, menentukan fungsi keanggotaan masing-masing atribut berdasarkan nilai probabilitas. Terakhir, menentukan attribute value dari atribut quantity, relationship dan same location pada data testing menggunakan fungsi keanggotaan masing-masing atribut. Dalam penentuan bobot fraud, ditentukan juga bobot penting atribut dan bobot perilaku berdasarkan atribut yang teridentifikasi. Berdasarkan nilai threshold fraud, pelanggaran SOP yang terjadi ditentukan sebagai fraud atau tidak.   Kata kunci: Fraud, transaksi, online, attribute value, SOP, fuzzy