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Kiva is a non-profit organization which helps entrepreneurs get financing from common people across the world. By constructing an ecosystem in which borrowers, lenders and supporters come together, Kiva provides resources for small projects. In order make the ecosystem available to as many people as possible, small amount investments are available for anyone to fund and help others start their projects. Single party oldenburg 2016 minimum investment needed to participate is 25 American dollars.
Kiva has created partnerships with other non-profit organizations and microfinance institutions, single party oldenburg 2016, the latter of which are local organizations that are working closely with their communities. The system gains deeper knowledge of the projects through the field partners, whom are responsible for underwriting this process. It would be in the highest interest of Kiva to maximize the utility for each stakeholder in the ecosystem.
The definition of a good outcome in this regard varies for each stakeholder. A borrower wants to be certain that his funding needs are met. The lender may have different goals, making the benefits for this party different. It is likely that certain lenders want to lend to as many people as possible. In these cases, good repayment rates would help, so that these persons can continue lending, since resources are limited. There may be other goals for a lender, like wanting single party oldenburg 2016 support specific countries or activities, while others may wish to fund one project at a time.
Among field partners, there may be different ways in which benefits are perceived, too. Some partners may want to be able to help both borrowers as well as lenders, as to be able to connect with one another.
For those who fund the borrower in advance, the main concern would be concentrated on getting the funding from Kiva. Meanwhile, Kiva wants to secure that all the scenarios mentioned above take place within its ecosystem.
This section discusses related works on previous analysis single party oldenburg 2016 regards to Kiva data and recommendations for microfinance. Team membership can improve the amount invested by lenders in a significant way, but does not affect the frequency in which a lender is actively funding projects [1].
Chen, Roy et al. Working single party oldenburg 2016 Kiva, they implemented a random test, consisting of 22, experiments. The authors concluded that goal-setting and coordination are effective mechanisms to increase both lender activity, as well as the invested amount. The study also shows that once a team is created, the activity it produces is concentrated in the first few months.
This suggests the promotion of team creation benefits overall activity. While the forming of teams may promote overall activity, this is not an everlasting reaction. show that the activity of a team is high during the initial stage, and then has a rapid decline.
According to Choo, J. et al. Relevant variables in this are, amongst others, location, single party oldenburg 2016, gender, and field partner reliability. The paper presents a model for team recommendation to lenders who have no affiliation with a team. To measure the performance, a rank is created for each lender, with a maximum ranking value of 1. On average, the model ranks as a 0.
In this paper, I propose a recommender algorithm for Kiva, whose goal is to improve activity, by recommending teams to lenders. There are two main approaches to recommender systems: content-based filtering and collaborative filtering. Content-based approaches use data created within a system, as to be able to provide recommendations for its users. If the system handles products, then it would single party oldenburg 2016 information about the product, such as category, price, color, single party oldenburg 2016, brand and more, to match the user profile, and then select some products to be suggested to that particular user.
The collaborative filtering method uses a similar mechanism between users, single party oldenburg 2016, herewith suggesting products to each user. In a system where two users are similar because they, for example, single party oldenburg 2016, both liked similar movies the system would suggest something to one user, based on the information of what the second user has seen and liked.
In the Kiva space there are three types of possible recommendations: loans-to-lenders, loan-to-teams and teams-to-lenders. All three recommendation approaches are possible. Since Single party oldenburg 2016 want to improve single party oldenburg 2016, I would want to recommend the teams-to-lenders or loans-to-teams. Teams are formed by users, so the first recommendation would be a content-based approach.
The loan-to-teams recommendation can be defined as a collaborative approach, since we need to see what other teams or lenders are doing in order to make recommendations to teams or lenders. Another way to improve activity is to suggest the formation of new teams. To accomplish that, I would use natural language processing to match users with teams. The attributes within the relations include geo-spatial, categorical, continuous, and unstructured text data.
Regarding stakeholders, the attributes contained are as followed: for the lenders, the data has information regarding location, occupation, sign up date, and loan count, as well as information on the number of loans funded by the user, its invitee count, and the number of invitations sent to other users to fund a loan, because the latter is one of the reasons to be a part of Kiva. The team data has category selected from a list of options provided by the system, described as free text loan, because this is a brief description of the overall team goal, loan count, loan amount, member count, membership type open or closeddate of creation and location, single party oldenburg 2016.
There are no restrictions to join a team with regards to location, but it helps to find affinities: when a new user would like to join a team, the region he or she is in could become one of the first reasons to join. Loan data makes up the largest relation, as it includes the status of the loan with detailed information about delinquency rate, repayment status, single party oldenburg 2016 and more. Activity is a sub sector type of attribute, loan use as a free text to state the purpose of the loan, location, currency and amount.
In addition, the data set has the relations between lenders and teams, lenders to loan, which are many-to-many, single party oldenburg 2016. A lender is not required to have a team affiliation, nor is he restricted to join only one team. There may be lenders that have joined several teams. The two main relationships I am interested in are:, single party oldenburg 2016. General statistics of the datasets are compared in Figure 1.
Teams are very important in the Kiva ecosystem. I know that promoting team creation improves activity, leading to more funding with more frequency. Kiva would benefit from recommending teams to users that have never joined a team, or by matching lenders to promote team formation. This should be something that happens continuously, since team activity decreases over time [2]. At the initial phase of experimentation and review of related work, I was focused on analyzing the relationship between the reasons to loan as stated by the lenders and the objective of each separate team.
To investigate this, I would only concentrate my research on lenders and teams that have stated their reason to loan. Taking that constrain into consideration, the team data gathered from the Kiva API corresponds with the data of teams created within the same time space as the lenders in the dataset.
There single party oldenburg 2016 11, teams within that space, making up a total of On average, a team has 32 members, with a standard deviation of and a median of 4 members.
Figure 2 shows team logarithmic distribution by single party oldenburg 2016 count. There are two types of teams: those that are open for anyone to join, and those that require prospective members to be approved by the administrators.
Each type is thus identified as either open or closed. Which type of team membership contributes more to Kiva? Closed teams in average fund loans, while open teams invest inwith a deviation of 4, and 13, loans respectively.
This proves that single party oldenburg 2016 teams contribute to more loans more often, which leads to increased activity. Liu, Y. The next question would be revolving around the terms of the lent amount. Which type of membership gives more per loan? Since the data does not reveal how much each lender gives with each loan, most Kiva-related papers make the assumption that each lender provided an equal amount to each loan, single party oldenburg 2016.
That is the same amount lent as for teams of the closed membership type. In terms of the amount lent, there is no visible difference in the amount derived by membership type, single party oldenburg 2016. This leads us to the same findings as other related work.
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