The paper presents two algorithms as a solution to the problem of identifying fraud intentions of a customer. Their purpose is to generate variables that contribute to fraud models’ predictive power improvement. In this article, a novel approach to the feature engineering, based on anomaly detection, is presented. As the choice of statistical model used in the research improves predictive capabilities of a solution to some extent, most of the attention should be paid to the choice of proper predictors. The main finding of the research is that model enrichment with additional predictors leads to the further improvement of predictive power and better interpretability of anti-fraud model. The paper is a contribution to the fraud prediction problem but the method presented may generate variable input to every tool equipped with variableselection algorithm. The cost is the increased complexity of the models obtained. The approach is illustrated on a dataset from one of the European banks.

The contribution main from this research is modularity and better processing time in detecting community by using K-1 coloring. Testing performed on transaction datasets remittance on P2P platforms where the Louvain Coloring algorithm is better in comparison to Louvain Algorithm Data used is data transfer transactions made by customers on the P2P Online platform. The data is the User data that has information transfer transactions, Card data that has information card, IP data that has IP information, and Device data that has information device. Every user owns unique 128-bit identification, and other nodes representing card, device, and IP are assigned a random UUID. The Device node has the guide, and device properties. IP nodes only have property guide and node User has property fraud Money Transfer, guide, money Transfer Error Cancel Amount, first Charge back Date. Each node has a unique 128-bit guide, with the amount whole of as many as 789,856 nodes. Application technique K-1 staining on Louvain algorithm shows enhancement value modularity and better processing time for detecting community on the network large scale. Through a series of exercises and tests carried out in various scenarios, it shows that the experiments carried out in this paper, namely the Louvain Coloring algorithm, are more effective and efficient than the Louvain algorithm in scenario 1,3, and 5 meanwhile For Scenarios 2 and 4 Louvain Algorithm is better.

This article discusses the challenges and problems caused by predatory journals in scholarly communication. By focusing on the editorial practices, I describe the case of Dr. Anna Fraud (Anna O. Szust in Polish): a bogus scientist created by four Polish scholars. Dr. Fraud became a member of editorial boards in over 40 scientific journals although she is a fake person and, obviously has no experience in journal editing. The present paper aims to show that scientist always have to care about a quality control and a peer review system. The article concludes with a presentation of the ‘Think. Check. Submit’ Initiative which provides useful and helpful tools for analysing journals by potential contributors.

The aim of this study is to examine the importance of the decision in Engelhart CTP (US) LLC v Lloyd’s Syndicate 1221 with regard to the insurance of economic losses and non-existent goods in the context of all-risks cover. It also strives to analyse to what extent the principles of construction described in this decision are applicable to the interpretation of all risks cover in marine insurance disputes subject to Polish law. Assureds and beneficiaries of cargo policies are defrauded into taking up false documents for non-existent goods yet Polish law fails to address the questions which arise in such situations. It is thus important not only to investigate and evaluate the answers provided by English law but also to explore if and how Polish law may employ them. It follows from this study that Engelhart may be used in construction of all-risks policies subject to Polish law in most scenarios.