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논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2020.8
- 수록면
- 63 - 105 (43page)
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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%.
목차
Abstract
INTRODUCTION
RELATED LITERATURE
GEOLOCATION NETWORK AND GEOSIMILARITY
EMPIRICAL ANALYSIS
PREDICTION MODEL AND RESULTS
DISCUSSION AND CONCLUSION
References
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UCI(KEPA) : I410-ECN-0101-2020-324-001158444