Academic SeminarYour Movement in a City Tells Your Credit: Credit Default Prediction based on Geolocation Information
- 2019-05-02 ~ 2019-05-02
- Supex Building, Lecture Room 101
- School of Management Engineering
We would like to invite you to participate in Management Engineering(ME) Seminar.
1. When: May 2nd (Thursday), 16:00~17:20
2. Where: Supex Building, Lecture Room 101
3. Speaker: Prof. Youngsok Bang (The Chinese University of Hong Kong)
4. Topic: Your Movement in a City Tells Your Credit: Credit Default Prediction based on Geolocation Information
5. Research field: IT Management
* Lecture will be delivered in Korean.
* Seminar materials: Abstract
Do people with a high credit default risk visit different locations in a city, compared to where people with a low credit default risk visit? We propose that geosimilarity risk and the geolocation network size of people comprise a critical classifier that predicts their credit default. Two people are defined as geosimilarity network (GSN) neighbors to each other if they share a visited location during a specific period. Based on the consumer-location and loan-repayment data from a leading FinTech company, we found that the GSN neighbors of a person who defaulted on a loan are approximately three times more likely to default compared to the average default rate, and are approximately 4.5 times more likely to default compared to the GSN neighbors of a person who has not defaulted. The geosimilarity risk and geolocation network size significantly explain credit defaults after controlling for traditional factors such as demographics, financial ability measures, and loan characteristics. In addition, incorporating these measures into the traditional model improves the prediction accuracy of credit default by approximately 9%.