<p>Online microloans provide attractive high-interest rate returns and can serve as a valuable investment diversification tool. However, effectively managing the inherent risks associated with these loans is a highly complex endeavor. To address this challenge, we introduce a comprehensive set of risk measures specifically tailored for unsecured equal-installment loans, considering the competing risks of default and prepayment. Our study compares two prominent survival analysis methods and argues for the superiority of the subdistribution approach. Although it is not widely studied in financial contexts, we prove it can provide better performance. We demonstrate, both theoretically and empirically, that the Subdistribution method can generate better survival probability estimation in the middle and late stages of a loan. Furthermore, based on better credit risk estimation, we propose passive and active loan screening strategies tailored to the unique business realities of online microloans. Empirical results show that the passive strategy outperforms the credit-score approach by a relative margin of 18.17%, and the active strategy yields a relative returns improvement over 20% compared to the credit-score approach. Our framework provides a robust approach to managing online microloans’ risks and returns and offers valuable insights and practical strategies for industry stakeholders.</p>