  This paper proposes a two-stage scoring approach to help lenders decide their
fund allocations in the peer-to-peer (P2P) lending market. The existing scoring
approaches focus on only either probability of default (PD) prediction, known
as credit scoring, or profitability prediction, known as profit scoring, to
identify the best loans for investment. Credit scoring fails to deliver the
main need of lenders on how much profit they may obtain through their
investment. On the other hand, profit scoring can satisfy that need by
predicting the investment profitability. However, profit scoring completely
ignores the class imbalance problem where most of the past loans are
non-default. Consequently, ignorance of the class imbalance problem
significantly affects the accuracy of profitability prediction. Our proposed
two-stage scoring approach is an integration of credit scoring and profit
scoring to address the above challenges. More specifically, stage 1 is designed
as credit scoring to identify non-default loans while the imbalanced nature of
loan status is considered in PD prediction. The loans identified as non-default
are then moved to stage 2 for prediction of profitability, measured by internal
rate of return. Wide and deep learning is used to build the predictive models
in both stages to achieve both memorization and generalization. Extensive
numerical studies are conducted based on real-world data to verify the
effectiveness of the proposed approach. The numerical studies indicate our
two-stage scoring approach outperforms the existing credit scoring and profit
scoring approaches.
