This paper provides a systematic comparison between two well-known Agile methodologies: Scrum, which is a framework of doing projects by allocating tasks into small stages called sprints, and Kanban, which is a scheduling system to manage the flow of work by means of visual signals. In this regard, both methodologies were reviewed to explore similarities and differences between them. Then, a focus group survey was performed to specify the preferable methodology for product development according to various parameters in the project environment including project complexity, level of uncertainty, and work size with consideration of output factors like quality, productivity, and delivery. Results show the flexibility of both methodologies in approaching Agile objectives, where Scrum emphasizes on the corporation of the customer and development teams with a focus on particular skills such as planning, organization, presentation, and reviewing which makes it ideal for new and complex projects where a regular involvement of the customer is required, whereas Kanban is more operative in continuous-flow environments with a steady approach toward a system improvement.
Turkish small-and medium-sized enterprises (SMEs) are exposed to fraud risks and creditor banks are facing big challenges to deal with financial accounting fraud. This study explores effectiveness of machine learning classifiers in detecting financial accounting fraud assessing financial statements of 341 Turkish SMEs from 2013 to 2017. The data are obtained from one of the leading creditor banks of Turkey. Highly imbalanced classes of 1384 nonfraudulent cases and 321 fraudulent cases (by 122 firms) are detected thus sampling techniques are used to mitigate class imbalance problem. Research methodology consists of two stages. First stage is data preprocessing wherein financial ratio calculation, feature selection methods for defining financial ratios with the greatest impact on fraudulent financial statements and two sampling methods of Synthetic Minority Oversampling Technique (SMOTE) as oversampling and undersampling are performed, respectively. Second stage is performance evaluation and comparison of classifiers wherein seven different classifiers (support vector machine, Naive Bayes, artificial neural network, K-nearest neighbor, random forest, logistic regression, and bagging) are executed and compared by using performance metrics. Classifiers are also compared without using any feature selection and/or sampling techniques. Results reveal that random forestwithout feature selection-oversampling model outperforms all other models.
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