Understanding and Promoting Micro-Finance Activities in Kiva.org Jaegul Choo*, Changhyun Lee*, Daniel Lee †, Hongyuan Zha*, and Haesun Park* *Georgia Institute of Technology † Georgia Tech Research Institute ACM International Conference on Web Search and Data Mining (WSDM) New York City, NY, USA 02/27/2014

Micro- Financing in Kiva.org 2

How Micro-Financing Works 3

Kiva 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 4

Modeling Micro-Financing Activities Target task Modeling likelihood of funding, π(f(u, l)), given a feature vector f for a lender (user) u and a loan l. Supervised learning Label: 1 if a lender u funded a loan l, and 0 otherwise Learner: gradient-boosting tree 5

Feature Generation Graph-based feature integration 6 u l

Cold-Start Problem What if lenders and loans have no links, e.g., brand-new lender and loan? 7 u l

Feature Alignment via Joint Nonnegative Matrix Factorization How it works Step 1: Learning mapping Step 2: Map data to an aligned space 8 Lender spaceLoan space Aligned space

ROC Curve Compared between different lender groups w.r.t. the number of previous loans, m. 9 AUC: 0.92 AUC: 0.79 Passive lender (m = 5)Active lender (m = 25)

Variable Importance Analysis Time between two consecutive loans is important. 10 AUC improvement over.5 when using only a particular feature group AUC degradation due to the exclusion of a particular feature group

Temporal Lending Behavior 11 People tend to keep funding loans continuously, but lose interest over time.

Temporal Lending Behavior 12 People tend to keep funding loans continuously, but lose interest over time. Loans are generally paid in half a year or a full year.

Temporal Lending Behavior 13 People tend to keep funding loans continuously, but lose interest over time. Loans are generally paid in half a year or a full year. Passive lenders often recycle money instead of spending more money.

Variable Importance Analysis Time between two consecutive loans is important. Loan delinquencies discourage passive lenders although they do not impact active lenders as much. 14 AUC improvement over.5 when using only a particular feature group AUC degradation due to the exclusion of a particular feature group

Variable Importance Analysis Time between two consecutive loans is important. Loan delinquencies discourage passive lenders although they do not impact active lenders as much. Lending teams greatly influence active lenders. 15 AUC improvement over.5 when using only a particular feature group AUC degradation due to the exclusion of a particular feature group

Performance Improvement due to Feature Alignment Aligned features Lenders’ occupational_info vs. loans’ description Lenders’ loan_because vs. loans’ description Baseline All the different textual fields are represented in a single space (using a common vocabulary set), and NMF is applied. 16

Aligned Topics People working at a school like to fund family-related loans. 17 Topic 1 a lender’s occupational infoa loan’s loan description teacher, preschool, math, librarian, school children, school, family, married, husband Topic 2 a lender’s occupational infoa loan’s loan description student, mba, college, graduate, university business, activities, entrepreneur, revenue

Aligned Topics People working at a school like to fund family-related loans. Students like to fund business-related loans. 18 Topic 1 a lender’s occupational infoa loan’s loan description teacher, preschool, math, librarian, school children, school, family, married, husband Topic 2 a lender’s occupational infoa loan’s loan description student, mba, college, graduate, university business, activities, entrepreneur, revenue

Comments on Other Papers 19 Inferring the Impacts of Social Media on Crowdfunding Associating social media with micro-financing activities, e.g., dynamics of team activities Is a Picture Really Worth a Thousand Words? - On the Role of Images in E-commerce Analyzing the effects of pictures in loan pages, e.g., borrowers’ picture

Summary 20 We modeled micro-financing activities at Kiva.org as a binary classification/regression problem. Graph-based feature integration Feature alignment via joint NMF We provided in-depth analysis and obtained knowledge about users’ lending behaviors. THANK YOU (Data set: Jaegul Choo