text: In this work, we analyze the problem of adoption of mobile money in Pakistan by using the call detail records of a major telecom company as our input. Our results highlight the fact that different sections of the society have different patterns of adoption of digital financial services but user mobility related features are the most important one when it comes to adopting and using mobile money services.
  @cite_4: The gender divide in the access to technology in developing economies makes gender characterization and automatic gender identification two of the most critical needs for improving cell phone-based services. Gender identification has been typically solved using voice or image processing. However, such techniques cannot be applied to cell phone networks mostly due to privacy concerns. In this paper, we present a study aimed at characterizing and automatically identifying the gender of a cell phone user in a developing economy based on behavioral, social and mobility variables. Our contributions are twofold: (1) understanding the role that gender plays on phone usage, and (2) evaluating common machine learning approaches for gender identification. The analysis was carried out using the encrypted CDRs (Call Detail Records) of approximately 10,000 users from a developing economy, whose gender was known a priori. Our results indicate that behavioral and social variables, including the number of input output calls and the in degree out degree of the social network, reveal statistically significant differences between male and female callers. Finally, we propose a new gender identification algorithm that can achieve classification rates of up to 80 when the percentage of predicted instances is reduced.
  @cite_7: About 2.6 billion people in the world do not have access to formal financial services, and yet one billion of them have a mobile phone. Branchless banking systems take advantage of increasingly ubiquitous real-time mobile communications networks to bring banking services into everyday retail stores, thereby alleviating the lack of banking infrastructure in the communities where poor people live and work. Most of these deployments are quite recent, hence there is a shortage of hard empirical evidence relating to them. However, one mobile banking scheme, MPESA in Kenya, has been phenomenally successful and has been a catalyst for much of the research done to date. In this article, we review the emerging literature on the definitions and model taxonomies employed in mobile banking; the status and drivers of global adoption of these schemes; the take-up and usage patterns of customers and their socioeconomic impact; and, finally, regulatory issues. Our objective is to help policymakers and practitioners in their continued efforts to create an enabling environment for branchless banking.
  @cite_8: We explore the extent to which gender disparities in Pakistan are reflected in the anonymized mobile phone logs of millions of Pakistani residents. Our analysis uses data capturing the communications behavior of several million individuals, for whom we observe the gender, but no additional demographic or personally identifying information. Here, we focus on validating aggregate regional patterns, correlating metrics derived from the mobile phone logs with socioeconomic statistics collected from more traditional sources. In these preliminary results, we observe a statistically significant relationship between districts with relatively high rates of female mobile phone penetration and districts that report high levels of gender parity in traditional surveys. However, this relationship is not uniform, and less developed regions exhibit a weaker correlation. We interpret these findings as suggestive evidence that such data can provide a novel perspective on gender dynamics in developing countries.
  @cite_1: Accurate and timely estimates of population characteristics are a critical input to social and economic research and policy. In industrialized economies, novel sources of data are enabling new approaches to demographic profiling, but in developing countries, fewer sources of big data exist. We show that an individual’s past history of mobile phone use can be used to infer his or her socioeconomic status. Furthermore, we demonstrate that the predicted attributes of millions of individuals can, in turn, accurately reconstruct the distribution of wealth of an entire nation or to infer the asset distribution of microregions composed of just a few households. In resource-constrained environments where censuses and household surveys are rare, this approach creates an option for gathering localized and timely information at a fraction of the cost of traditional methods.
  @cite_0: Churn prediction, or the task of identifying customers who are likely to discontinue use of a service, is an important and lucrative concern of firms in many different industries. As these firms collect an increasing amount of large-scale, heterogeneous data on the characteristics and behaviors of customers, new methods become possible for predicting churn. In this paper, we present a unified analytic framework for detecting the early warning signs of churn, and assigning a "Churn Score" to each customer that indicates the likelihood that the particular individual will churn within a predefined amount of time. This framework employs a brute force approach to feature engineering, then winnows the set of relevant attributes via feature selection, before feeding the final feature-set into a suite of supervised learning algorithms. Using several terabytes of data from a large mobile phone network, our method identifies several intuitive - and a few surprising - early warning signs of churn, and our best model predicts whether a subscriber will churn with 89.4 accuracy.
  @cite_5: 
summary: The work on the adoption of digital financial services has been dominated by macroeconomic work related to regulatory issues around interoperability and the logistics of mobile money agents @cite_7 @cite_5 and a very few research papers have explored the adoption of mobile money from a quantitative perspective. However, mining of insights from mobile communication meta-data has been a popular area of research and some examples of work in this area include predicting the socioeconomic status @cite_1 , gender @cite_4 of mobile phone subscribers, customer churn behavior @cite_0 and analysis of gender disparities using social networks extracted from mobile communication logs @cite_8 .