TY - GEN
T1 - Does AI-based credit scoring improve financial inclusion? Evidence from online payday lending
AU - Wang, Hongchang
AU - Li, Chunxiao
AU - Gu, Bin
AU - Min, Wei
N1 - Publisher Copyright:
© 40th International Conference on Information Systems, ICIS 2019. All rights reserved.
PY - 1984
Y1 - 1984
N2 - Artificial intelligence (AI) has become ubiquitous in the consumer finance industry. One of the major AI applications in this industry is AI-based credit scoring models. We investigate whether AI applications improve financial inclusion, as measured by three seemingly contradictory metrics, i.e. approval rate, default rate, and false rejection rate. We cooperate with an AI solution provider whose AI-based credit scoring models are widely used by online lenders in China. Using data obtained from these online lenders, we find that AI-based credit scoring models increase approval rate and reduce default rate simultaneously, which enhances both the magnitude and the quality of financial inclusion. AI-based credit scoring models also tend to reduce false rejection rate, suggesting that they can help provide access to capital to previously underserved population. We plan to collect more data and conduct additional analyses in the future to enrich our current findings and explore for underlying mechanisms.
AB - Artificial intelligence (AI) has become ubiquitous in the consumer finance industry. One of the major AI applications in this industry is AI-based credit scoring models. We investigate whether AI applications improve financial inclusion, as measured by three seemingly contradictory metrics, i.e. approval rate, default rate, and false rejection rate. We cooperate with an AI solution provider whose AI-based credit scoring models are widely used by online lenders in China. Using data obtained from these online lenders, we find that AI-based credit scoring models increase approval rate and reduce default rate simultaneously, which enhances both the magnitude and the quality of financial inclusion. AI-based credit scoring models also tend to reduce false rejection rate, suggesting that they can help provide access to capital to previously underserved population. We plan to collect more data and conduct additional analyses in the future to enrich our current findings and explore for underlying mechanisms.
KW - Artificial intelligence
KW - Credit scoring models
KW - False rejection rate
KW - Financial inclusion
KW - Financial technologies
UR - http://www.scopus.com/inward/record.url?scp=85084711212&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85084711212&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85084711212
T3 - 40th International Conference on Information Systems, ICIS 2019
BT - 40th International Conference on Information Systems, ICIS 2019
PB - Association for Information Systems
T2 - 40th International Conference on Information Systems, ICIS 2019
Y2 - 15 December 2019 through 18 December 2019
ER -