FinTech: Adoption and Consequences
Friday, Jan. 3, 2020 10:15 AM - 12:15 PM (PDT)
- Chair: Stephan Siegel, University of Washington
FinTech and Credit Scoring for the Millennials: Evidence using Mobile and Social footprints
AbstractUsing a unique and proprietary loan-level data from a large Fintech lending firm in India, we analyze whether unstructured data pertaining to a consumer's social and mobile footprint can act as a substitute for traditional credit bureau scores. We find that the mobile footprint of an individual outperforms the credit score in predicting loan approvals and defaults. Importantly, including measures of borrower's "deep social footprints" based on call logs significantly improves default prediction. We use machine learning based prediction counterfactual analysis to predict the loan outcome for borrowers who were denied credit, perhaps due to the lack of traditional credit scores. We show that using alternate credit scoring using the mobile and social footprints can expand credit as well as reduce overall default rate. Our study has implications for expanding access to credit to those who do not have a credit history but who leave a large trace of unstructured information on their mobile phones that can be used to predict loan outcomes.
Crowdsourcing Financial Information to Change Spending Behavior
AbstractWe document five effects of providing individuals with crowdsourced spending information about their peers (individuals with similar characteristics) through a FinTech app. First, users who spend more than their peers reduce their spending significantly, whereas users who spend less keep constant or increase their spending. Second, users' distance from their peers' spending affects the reaction monotonically in both directions. Third, users' reaction is asymmetric -- spending cuts are three times as large as increases. Fourth, lower-income users react more than others. Fifth, discretionary spending drives the reaction in both directions and especially cash withdrawals, which are commonly used for incidental expenses and anonymous transactions. We argue Bayesian updating, peer pressure, or the fact that bad news looms larger than (equally-sized) good news cannot alone explain all these facts.
Consumers’ Financial Constraints, Lawsuit Decisions, and the Civil Justice System
AbstractWhen consumers encounter severe personal injuries (PI) and hence were disabled to work, they lose income. Insurance claims take long and most U.S. households are not financially healthy to wait that long and hence choose to accept a lower and faster settlement sometimes. Consumer litigation funding (CLF) as financial innovation and new asset class provides non-recourse loans to consumers during the claim and litigation to smooth consumption. Using novel county-level lawsuit data, I explore the extensive margin of consumers’ claims and test whether having access to such funding affect consumers’ lawsuit decisions and hence increases the likelihood of consumers bringing the case to the courts. I find that access to CLF increase the likelihood of consumers bringing PI cases to the courts and lowers the plaintiff winning rates of lawsuits that go to courts. This study provides initial evidence on how innovation in the financial sector could affect the consumers' real economic decision and the justice system.
- G2 - Financial Institutions and Services