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.
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 .
@cite_4 @cite_7 @cite_8 @cite_1 @cite_0 @cite_5
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Billions of people around the world live without access to banks or other formal financial institutions. In the past several years, many mobile operators have launched "Mobile Money" platforms that deliver basic financial services over the mobile phone network. While many believe that these services can improve the lives of the poor, in many countries adoption of Mobile Money still remains anemic. In this paper, we develop a predictive model of Mobile Money adoption that uses billions of mobile phone communications records to understand the behavioral determinants of adoption. We describe a novel approach to feature engineering that uses a Deterministic Finite Automaton to construct thousands of behavioral metrics of phone use from a concise set of recursive rules. These features provide the foundation for a predictive model that is tested on mobile phone operators logs from Ghana, Pakistan, and Zambia, three very different developing-country contexts. The results highlight the key correlates of Mobile Money use in each country, as well as the potential for such methods to predict and drive adoption. More generally, our analysis provides insight into the extent to which homogenized supervised learning methods can generalize across geographic contexts. We find that without careful tuning, a model that performs very well in one country frequently does not generalize to another. 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. Scholars are increasingly citing electronic web references which are not preserved in libraries or full text archives. WebCite is a new standard for citing web references. To webcite a document involves archiving the cited Web page through www.webcitation.org and citing the WebCite permalink instead of (or in addition to) the unstable live Web page. This journal has amended its instructions for authors accordingly, asking authors to archive cited Web pages before submitting a manuscript. Almost 200 other journals are already using the system. We discuss the rationale for WebCite, its technology, and how scholars, editors, and publishers can benefit from the service. Citing scholars initiate an archiving process of all cited Web references, ideally before they submit a manuscript. Authors of online documents and websites which are expected to be cited by others can ensure that their work is permanently available by creating an archived copy using WebCite and providing the citation information including the WebCite link on their Web document(s). Editors should ask their authors to cache all cited Web addresses (Uniform Resource Locators, or URLs) prospectively before submitting their manuscripts to their journal. Editors and publishers should also instruct their copyeditors to cache cited Web material if the author has not done so already. Finally, WebCite can process publisher submitted citing articles (submitted for example as eXtensible Markup Language [XML] documents) to automatically archive all cited Web pages shortly before or on publication. Finally, WebCite can act as a focussed crawler, caching retrospectively references of already published articles. Copyright issues 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. In a document network such as a citation network of scientific documents, web-logs, etc., the content produced by authors exhibits their interest in certain topics. In addition some authors influence other authors' interests. In this work, we propose to model the influence of cited authors along with the interests of citing authors. Moreover, we hypothesize that apart from the citations present in documents, the context surrounding the citation mention provides extra topical information about the cited authors. However, associating terms in the context to the cited authors remains an open problem. We propose novel document generation schemes that incorporate the context while simultaneously modeling the interests of citing authors and influence of the cited authors. Our experiments show significant improvements over baseline models for various evaluation criteria such as link prediction between document and cited author, and quantitatively explaining unseen text. A citing sentence is one that appears in a scientific article and cites previous work. Citing sentences have been studied and used in many applications. For example, they have been used in scientific paper summarization, automatic survey generation, paraphrase identification, and citation function classification. Citing sentences that cite multiple papers are common in scientific writing. This observation should be taken into consideration when using citing sentences in applications. For instance, when a citing sentence is used in a summary of a scientific paper, only the fragments of the sentence that are relevant to the summarized paper should be included in the summary. In this paper, we present and compare three different approaches for identifying the fragments of a citing sentence that are related to a given target reference. Our methods are: word classification, sequence labeling, and segment classification. Our experiments show that segment classification achieves the best results.