Start Date
10-12-2017 12:00 AM
Description
Microfinance based institutions have emerged as a potential solution to the financial exclusion problem in developing economies around the world. A key challenge facing such micro-lending firms is assessing the credit risk of borrowers, owing to the lack of formal financial histories and collaterals. A number of micro-lending companies have, therefore, started leveraging social media and digital communication data from applicants to assess their ability and willingness to repay loans. In our study, we demonstrate a novel approach of leveraging online and offline behavior sequences, as captured from the borrowers’ browsing logs and mobility traces to accurately predict the borrowers’ creditworthiness. Our preliminary results show that using such sequence data, we can provide micro-lending firms with a cheap and reliable strategy for assessing credit risk of borrowers at the time of loan creation. We contend that such big-data based strategies are critical to the sustainability of micro-lending institutions.
Recommended Citation
Mehrotra, Rishabh; Bhattacharya, Prasanta; Tan, Tianhui; and Phan, Tuan, "Predictive Power of Online and Offline Behavior Sequences: Evidence from a Micro-finance Context" (2017). ICIS 2017 Proceedings. 28.
https://aisel.aisnet.org/icis2017/DataScience/Presentations/28
Predictive Power of Online and Offline Behavior Sequences: Evidence from a Micro-finance Context
Microfinance based institutions have emerged as a potential solution to the financial exclusion problem in developing economies around the world. A key challenge facing such micro-lending firms is assessing the credit risk of borrowers, owing to the lack of formal financial histories and collaterals. A number of micro-lending companies have, therefore, started leveraging social media and digital communication data from applicants to assess their ability and willingness to repay loans. In our study, we demonstrate a novel approach of leveraging online and offline behavior sequences, as captured from the borrowers’ browsing logs and mobility traces to accurately predict the borrowers’ creditworthiness. Our preliminary results show that using such sequence data, we can provide micro-lending firms with a cheap and reliable strategy for assessing credit risk of borrowers at the time of loan creation. We contend that such big-data based strategies are critical to the sustainability of micro-lending institutions.