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This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]

This paper examines how the use of multiple platforms is tied to awareness of algorithms. It builds on the premise that users interact with ecologies or environments of technologies rather than single platforms. The study also supplements work on algorithmic awareness by implementing a mixed-method study to account for how Costa Rican users of Netflix and Spotify understood and related to the algorithms of these platforms. This study combined a survey of 258 participants and 21 in-depth semistructured interviews. Findings demonstrate that multi-platform users were more aware of algorithms and carried out more practical actions to obtain algorithmic recommendations than single-platform users. Although user type did not predict participants' attitudes towards algorithmic recommendations, higher levels of awareness were associated with more positive attitudes towards algorithms. The study also shows that differences in levels of awareness explained users' emotional arousal derived from algorithms. [ABSTRACT FROM AUTHOR]

The research field of crisis informatics examines, amongst others, the potentials and barriers of social media use during conflicts and crises. Social media allow emergency services to reach the public easily in the context of crisis communication and receive valuable information (e.g. pictures) from social media data. However, the vast amount of data generated during large-scale incidents can lead to issues of information overload and quality. To mitigate these issues, this paper proposes the semi-automatic creation of alerts including keyword, relevance and information quality filters based on cross-platform social media data. We conducted empirical studies and workshops with emergency services across Europe to raise requirements, then iteratively designed and implemented an approach to support emergency services, and performed multiple evaluations, including live demonstrations and field trials, to research the potentials of social media-based alerts. Finally, we present the findings and implications based on semi-structured interviews with emergency services, highlighting the need for usable configurability and white-box algorithm representation. [ABSTRACT FROM AUTHOR]