Mendelson, H., & Zhu, M. (2024). Optimal Information Acquisition Strategies: The Case of Online Lending. arXiv preprint arXiv:2410.05539v1.
This paper investigates the optimal strategies for online lenders to acquire information about borrowers' creditworthiness and maximize profitability in a dynamic lending environment. The research aims to determine whether a lean experimentation (LE) approach, characterized by gradual increases in loan amounts, or a single grand experiment (GE) approach, involving a one-time assessment of creditworthiness, is more effective.
The authors develop a discrete-time, infinite-horizon, dynamic control model to represent the online lending process. They analyze the model under two scenarios: an exogenous interest rate set by the market and an endogenous interest rate determined by the lender. The optimal lending policies are derived using Bellman equations and analyzed based on the monotonicity properties of the demand elasticity for loans.
The choice between LE and GE depends on the lender's ability to adjust interest rates and the sensitivity of borrower demand to interest rate changes. The research highlights the importance of understanding demand elasticity in designing effective information acquisition strategies for online lending.
This study provides valuable insights for online lenders in optimizing their lending practices and maximizing profitability. It also contributes to the broader field of information acquisition by demonstrating the context-dependent nature of optimal strategies.
The model assumes a simplified lending environment with a focus on unsecured loans. Future research could explore the implications of different loan types, collateralization, and competitive market dynamics on information acquisition strategies.
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