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Mingxi Zhu (OIT): Dynamic Exploration and Exploitation: The Case of Online Lending

Event Details:

Thursday, October 13, 2022
5:00pm - 6:00pm PDT

Title: Dynamic Exploration and Exploitation: The Case of Online Lending


Abstract:
We study exploration/exploitation tradeoffs in the context of online lending. Unlike traditional exploration/exploitation contexts, where the cost of exploration is an opportunity cost of lost revenue or some other implicit cost, in the case of unsecured online lending, the lender effectively gives away money in order to learn about the borrower's ability to repay. In our model, the lender maximizes the expected net present value of the cash flow she receives by dynamically adjusting the loan amounts and the interest rate as she learns about the borrower's unknown income. The lender has to carefully balance the trade-offs between earning more interest when she lends more and the risk of default, and we provided the optimal dynamic policy for the lender.  When the demand elasticity is a decreasing function of the discount rate, or when the discount rate is set exogenously, the optimal policy is characterized by a large number of small experiments with increasing repayment amounts. When the demand elasticity is constant or when it is an increasing function of the discount rate and the hazard function of the consumer's income distribution is increasing, we obtain a two-step optimal policy: the lender performs a single experiment and then, if the borrower repays the loan, offers the same loan amount and discount rate in each subsequent period without any further experimentation. We further provide the implications under the optimal policies, including the impact of the income variability, the value of information and the consumer segmentation. We provide the implications under the optimal policies, including the impact of the income variability, the value of information and the consumer segmentation. Lastly, we extend the methodology to analyze the Buy-Now-Pay-Later business model and provide the policy suggestions.


Bio:
Mingxi is a final year Ph.D. student in the Operations, Information & Technology field at Stanford Graduate School of Business. She is co-advised by Professor Haim Mendelson and Professor Yinyu Ye. Her research interest lies in developing large scale optimization algorithms to facilitate online platforms operations and revenue management, with applications in machine learning, pricing/mechanism design and empirical marketing. She will be on the 2022-2023 academic job market. 
 

Personal Website:

https://sites.google.com/view/mingxizhu

 

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