Security and privacy (S&P) advice plays a crucial role in how people stay safe online. While prior work shows that the plethora of advice from varied sources makes it difficult for users to prioritize advice, the insights are primarily based on studies conducted in Western contexts. Other work shows that users outside the West have different S&P needs and thus, we cannot simply rely on advice curated in the West to generalize to the majority world - regions of Africa, Asia, Latin America, and the Middle East, where most of the world's population lives. We fill this gap by investigating S&P advice across nine majority world countries via 70 semi-structured interviews with local experts: cybercafe operators, tech repair specialists, and other community figures that people commonly rely on for tech support and S&P advice. We find that the advice provided by local experts in the majority world largely matches the advice they provide to their constituents and the advice from the West. However, we surface various significant barriers that hinder majority world users from implementing advice, including economic constraints, language barriers, and social friction from taking protective measures. Our findings further show how factors such as social norms and gender shape advice practices, e.g., by driving gendered advice-seeking. We discuss how S&P advice in the majority world can be improved and reflect on how the S&P community can better engage with local communities in conducting similar research.

Research on mobile money smishing is hindered, especially in the African context, as there is a lack of data. The absence of datasets and the fact that Mobile Network Operators don’t maintain such data pose a significant challenge. Additionally, the absence of a data collection infrastructure further complicates the data acquisition process. In response to this challenge, we developed a scalable and cost-effective honeynet infrastructure tailored for the efficient collection of organic Short Message Service (SMS) messages. The innovative approach involves harnessing the capabilities of Raspberry Pi units, USB multipliers, SIM cards from MNOs and GSM modems to create a scalable and adaptable solution. This aims to enhance smishing data collection, facilitating more efficient research into mobile money smishing.

Digital financial services have catalyzed financial inclusion in Africa. Commonly implemented as a mobile wallet service referred to as mobile money (MoMo), the technology provides enormous benefits to its users, some of whom have long been unbanked. While the benefits of mobile money services have largely been documented, the challenges that arise—especially in the interactions between human stakeholders—remain relatively unexplored. In this study, we investigate the practices of mobile money users in their interactions with mobile money agents. We conduct 72 structured interviews in Kenya and Tanzania (n=36 per country). The results show that users and agents design workarounds in response to limitations and challenges that users face within the ecosystem. These include advances or loans from agents, relying on the user-agent relationships in place of legal identification requirements, and altering the intended transaction execution to improve convenience. Overall, the workarounds modify one or more of what we see as the core components of mobile money: the user, the agent, and the transaction itself. The workarounds pose new risks and challenges for users and the overall ecosystem. The results suggest a need for rethinking privacy and security of various components of the ecosystem, as well as policy and regulatory controls to safeguard interactions while ensuring the usability of mobile money.

This paper covers a scoping review to establish the breadth of alternative credit scoring literature. The field is nascent and gaining popularity due to the crucial role alternative data is playing to accelerate financial inclusion. Historically, evaluating creditworthiness required availability of past financial activity such as loan repayment. Such stringent requirements rendered people with little or no financial history ‘credit invisible’. Advancements in Artificial Intelligence and Machine Learning have enabled scoring algorithms to work with non-financial data such as digital footprints from mobile devices and psychometric data to compute credit scores. Although the largest portion of ‘credit invisibles’ are in developing economies, research in the area is predominantly originating from developed economies and most alternative credit scoring models are trained with data from developed economies. There is need for more research from developing contexts and utilization of alternative data from populations with a smaller digital footprint.