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

jaegul.choo@cc.gatech.edu http://www.cc.gatech.edu/~joyfull/

2014 ACM International Conference on Web Search and Data Mining (WSDM)

New York City, NY, USA02/27/2014

Micro-Financing

in Kiva.org

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How Micro-Financing Works

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Kiva Datahttp://tinyurl.com/kiva-matlab-data

EntitiesLender (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

GraphsLender-loan (12M edges): who funds which loanLender-team (300k edges): who is a member of which team

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Modeling Micro-Financing Activities

Target taskModeling likelihood of funding, π(f(u, l)), given a feature vector f for a lender (user) u and a loan l.

Supervised learningLabel: 1 if a lender u funded a loan l, and 0 otherwiseLearner: gradient-boosting tree

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Feature Generation

Graph-based feature integration

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ul

Lender

LoanLoanLoan

LoanLoanTeam

LoanLoanPartner

LoanLoanBorrower

LoanLoanTeam

Partner

LoanLoanBorrower

LoanLoanLender

Loan

Lender-specific Matching Loan-

specific

Features

𝒗𝒖 𝒗 𝒍𝒗𝒖.∗𝒗 𝒍

Lender

LoanLoanLoan

LoanLoanTeam

LoanLoanPartner

LoanLoanBorrower

LoanLoanTeam

Partner

LoanLoanBorrower

LoanLoanLender

Loan

Lender-specific Matching Loan-

specific

Features

Lender

LoanLoanLoan

LoanLoanTeam

LoanLoanPartner

LoanLoanBorrower

LoanLoanTeam

Partner

LoanLoanBorrower

LoanLoanLender

Loan

Lender-specific Matching Loan-

specific

Features

Cold-Start Problem

What if lenders and loans have no links, e.g., brand-new lender and loan?

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ul

Feature Alignment via Joint Nonnegative Matrix Factorization

How it works

Step 1: Learning mapping

Step 2: Map data to an aligned space

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Lender space Loan space Aligned space

2 2 2

, , ,min || || || || || ||

u u l l

T Tu u u F l l l F u l F

W H W HA W H A W H H H

2 20 0

min || || and min || ||u l

T Tu u u l l l

h ha W h a W h

ROC Curve

Compared between different lender groups w.r.t. the number of previous loans, m.

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AUC: 0.92 AUC: 0.79

Passive lender (m = 5) Active lender (m = 25)

Variable Importance Analysis

Time between two consecutive loans is important.

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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

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People tend to keep funding loans continuously, but lose interest over time.

Temporal Lending Behavior

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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

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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.

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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.

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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 featuresLenders’ occupational_info vs. loans’ descriptionLenders’ loan_because vs. loans’ description

BaselineAll 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.

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Topic 1

a lender’s occupational info a loan’s loan description

teacher, preschool, math, librarian, school

children, school, family, married, husband

Topic 2

a lender’s occupational info a 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.

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Topic 1

a lender’s occupational info a loan’s loan description

teacher, preschool, math, librarian, school

children, school, family, married, husband

Topic 2

a lender’s occupational info a loan’s loan description

student, mba, college, graduate, university

business, activities, entrepreneur, revenue

Comments on Other Papers

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Inferring the Impacts of Social Media on CrowdfundingAssociating 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

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We modeled micro-financing activities at Kiva.org as a binary classification/regression problem.

Graph-based feature integrationFeature alignment via joint NMF

We provided in-depth analysis and obtained knowledge about users’ lending behaviors.

THANK YOU(Data set: http://tinyurl.com/kiva-matlab-data)

Jaegul Choojaegul.choo@cc.gatech.edu

http://www.cc.gatech.edu/~joyfull/