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On-demand work applications (ODWAs), a type of digital platforms (DPs), are technology-enabled intermediaries for gig workers. In this paper, we study these platforms and their impact on women belonging to the low socio-economic section in a developing economy. These women act as primary unpaid workers while being expected to make an economic contribution. Hence, they suffer from time scarcity where they need to find mechanisms to generate economic success from the limited time they have, in a largely informal economy. We employed the theoretical lens of affordances to delineate the affordances that are provided by the various features of ODWAs and their impact on life satisfaction of women. We conducted a mixed method study using interviews of 20 female gig workers, supplemented by a survey of 927 workers on one of the largest ODWAs in India. Our analysis points to the sense of identity and individuality that these platforms provide as well as lowering the market boundaries for greater inclusion. Our research is significant from both, the information systems and inclusion research perspectives, as it contributes to the theoretical understanding of the impact of DPs on breaking societal structures and providing avenues to marginalised sections. [ABSTRACT FROM AUTHOR]

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]