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To Gather Together for a Better World: Leveraging Communities in Micro-Lending Recommendation Jaegul Choo*, Daniel Lee , Bistra Dilkina*, Hongyuan Zha*, and Haesun Park* *Georgia Institute of Technology Georgia Tech Research Institute 2014 International World Wide Web Conference (WWW)

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Micro- Financing in Kiva.org 2

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How Micro-Financing Works 3

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Impact of Lending Teams 4

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Overview Main idea Understanding and Leveraging lending teams in modeling lending activities Outline Modeling lending activities Lending activity prediction Analysis on team behaviors Team affiliation prediction 5

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Kiva Data http://tinyurl.com/kiva-matlab-data http://tinyurl.com/kiva-matlab-data Entities Lender (1M): sign-up date, loan_because, occupation, location, Loan (560K) : description, amount, location, sector, Lending team (25K): type, #members, #funded loans, Field partner (250): due-diligence type, delinquency rate, location, Borrower (1M): name, gender Graphs Lender-loan (12M edges): who funds which loan Lender-team (300k edges): who is a member of which team 6

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Modeling Lending Activities Target task Given a lender (user) u and a loan l, and his/her team affiliation t, Modeling likelihood/density of funding, t (f(u, l)), where f(u, l) is a feature vector for (u, l). 7

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Feature Generation Graph-based Feature Integration 8 u l

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Modeling Lending Activities Target task Given a lender (user) u and a loan l, and his/her team affiliation t, Modeling likelihood/density of funding, t (f(u, l)), where f(u, l) is a feature vector for (u, l). Supervised learning Label: 1 if a lender u funded a loan l, and 0 otherwise Model: Maximum-entropy distribution model 9

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Why Maximum-Entropy Distribution (Maxent) Model? Negative samples are NOT really negative. No funding does NOT mean that s/he dislikes the loan. Maxent model Considers labeled data as presence-only data (rather than presence-vs-absence data) Lets give as much probability as possible to negative samples Density: Optimization: 10

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Ensemble Model Based on Team-Specific Models Motivation What if team information is NOT known for a lender u ? Weighted Stacking Approach 11 L1-regularized logistic regression

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Overview Main idea Leveraging lending teams in modeling lending activities Outline Modeling lending activities Lending activity prediction Analysis on team behaviors Team affiliation prediction 12

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Experimental Setup 1. Team and Data Selection We selected top 200 lending teams (70% of the total lending amount) For each team, we selected 5,000 lender-loan pairs where funding happened (positive) and 5,000 other pairs (negative). 200 team-specific models + no-team model (201 in total) 13

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Experimental Setup 2. Used Features 938 dimensions in total Textual (600 dimensions): a lender loan because and a loans loan description Loan sector (45 dimensions): industry of a loan, e.g., agriculture, food, retail, etc. Geo-location (228 dimensions): a lenders and loans location Loan delinquency/default (13 dimensions): delinquency/default rate of a lenders previous loans Field partner (33 dimensions): loan amount, rating Borrower (12 dimensions): a borrowers gender and has_picture Temporal information (7 dimensions): relative time difference between two consecutive loans of a lender 14

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Experimental Setup 3. Compared Methods Aggregate-data model: a single model using aggregated data (Team information is encoded as a 60-dimensional feature.) Team-specific model Ensemble model 15

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Prediction Result AUC values over 200 Teams 16 Loan feature Loan feature + Lender feature Loan feature + Lender feature + Correlation feature Aggregate- data Team- specific Ensemble Ensemble model works best.

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What Do Teams Care? Variable Importance Analysis Commonalities Time interval between two consecutive loans is important. Loan delinquencies highly discourage further lending. Differences Teams carefully choose loans depending on various aspects. 17

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Which Teams Care Which Aspect? Top Five Teams for Feature Groups Loan sector (Industry) KivaFriends - Agriculture Loans, Ravelry.com, 101 Cookbooks, Give Green - Environmental Loans, Thailand Geo-location Para Mexico, Philippines, Kiva Muslims, Kiva Detroit, Portugal Field partner Amici di Raffaele (Raphaels Friends), Woodlands, Compadres, Lauren Avezzie, Kiva Jews Borrower women empowering women, HALF THE SKY: Empowering Women, Georgia Southern Alumni, www.idu.cc, Tareto Maa 18

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Which Teams have Outlying Behaviors? Visualization of variable importance vectors (generated by principal component analysis) 19 Two outlying teams Expired Loans Late Loaning Lenders Time taken for a loan to be fully funded

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Overview Main idea Leveraging lending teams in modeling lending activities Outline Modeling lending activities Lending activity prediction Analysis on team behaviors Team affiliation prediction 20

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Team Affiliation Prediction Given a lender u and a team t, the likelihood L(u, t) is where l i u are the first c loans of u. Supervised Learning Features f(u, t): Similarity-weighted likelihood values Label: 1 if a lender u is affiliated with the team t Learner: Logistic regression 21

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Team Affiliation Prediction Results 22 Mean reciprocal rank (MRR) Similarity-weighted model.1482 (.0402) Direct team-specific model.0851 (.0365) Aggregate-data model.0548 (.0210) Random assignment.0294

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Conclusion and Future Work In summary, we analyzed lending teams in a micro-finance service, Kiva.org. Modeled team activities as a Maxent model Discovered diverse team characteristics Performed team affiliation prediction Future Work Social influence within a team 23

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Conclusion and Future Work In summary, we presented lending teams in a micro-finance service, Kiva.org. Kiva.org. Modeled team activities as a Maxent model Discovered diverse team characteristics Performed team affiliation prediction Future Work Social influence within a team 24 Kiva Christians Atheists, Agnostics, Co-lending graph between lenders

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Conclusion and Future Work In summary, we presented lending teams in a micro-finance service, Kiva.org. Modeled team activities as a Maxent model Discovered diverse team characteristics Performed team affiliation prediction Future Work Social influence within a team Compare and contrast between teams 25

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Conclusion and Future Work In summary, we presented characteristics of lending teams in a micro-finance service, Kiva.org. Modeled team activities as a Maxent model Discovered diverse behaviors of lending teams Performed team affiliation prediction Future Work Social influence within a team Compare and contrast between teams 26 Thai`Greece Common topics

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Conclusion and Future Work In summary, we presented lending teams in a micro-finance service, Kiva.org. Modeled team activities as a Maxent model Discovered diverse team characteristics Performed team affiliation prediction On-going/future Work Social influence within a team Compare and contrast between teams Evolution of lending teams 27 Thank you! Jaegul Choo jaegul.choo@cc.gatech.edu jaegul.choo@cc.gatech.edu http://www.cc.gatech.edu/~joyfull/ Currently on the Job Market