Inferring borrower network in a microfinancing framework (KIVA)

Abstract: Microfinance institutions (MFIs) play a considerable role in providing capital to micro businesses (MBs) through microcredit services. However, the interplay between MFIs and MB owners has been hindered by several factors, such as challenges with information sharing. The current study aims at identifying the specific challenges of microcredit services in Dar es Salaam, Tanzania, and also at determining the enabling factors, preferred features, and general requirements for a mobile technology solution to support the interplay between MFIs and MBs. The participants in the study were 91 MB owners and 22 MFI officers, and the data for the study were collected via a questionnaire. The study confirmed that the interaction, communication, and relationship between MB owners and MFIs is affected by various challenges. It was confirmed that digital technology can be used to address the identified challenges, and that a tailor-made mobile technology solution would be appropriate for supporting the interaction, communication, and relationship between MFIs and MBs. Gomera et al. User requirement for mobile microcredit services The African Journal of Information Systems, Volume 12, Issue 1, Article 1 2

Abstract: The problem of measuring "similarity" of objects arises in many applications, and many domain-specific measures have been developed, e.g., matching text across documents or computing overlap among item-sets. We propose a complementary approach, applicable in any domain with object-to-object relationships, that measures similarity of the structural context in which objects occur, based on their relationships with other objects. Effectively, we compute a measure that says "two objects are similar if they are related to similar objects:" This general similarity measure, called SimRank, is based on a simple and intuitive graph-theoretic model. For a given domain, SimRank can be combined with other domain-specific similarity measures. We suggest techniques for efficient computation of SimRank scores, and provide experimental results on two application domains showing the computational feasibility and effectiveness of our approach.

Abstract: Up to date, more than 15 billion US dollars have been invested in microfinance that benefited more than 160 million people in developing countries. The Kiva organization is one of the successful examples that use a decentralized matching process to match lenders and borrowers. Interested lenders from around the world can look for cases among thousands of applicants they found promising to lend the money to. But how can loan borrowers and lenders be successfully matched up in a microfinance platform like Kiva? We argue that a sophisticate recommender not only pairs up loan lenders and borrowers in accordance to their preferences, but should also help to diversify the distribution of donations to reduce the inequality of loans is highly demanded, as altruism, like any resource, can be congestible.In this paper, we propose a fairness-aware recommendation system based on one-class collaborative-filtering techniques for charity and micro-loan platform such as Kiva.org. Our experiments on real dataset indicates that the proposed method can largely improve the loan distribution fairness while retaining the accuracy of recommendations.