Wide and deep learning for peer-to-peer lending

Abstract

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.

A Deep Learning Based Online Credit Scoring Model for P2P Lending

“…Credit scoring is essentially a binary classification method. It is generally used to predict the default probability of loan applicants, and accordingly divides loan applicants into defaulters and non-defaulters [1]. Corresponding models of credit scoring have roughly gone through three development stages: linear discriminant method, statistical method, and machine learning method.…”

“…However, its inefficiency in learning over dense numerical features is a weakness [8]. Currently, NN has been well applied to the field of credit scoring, such as the wide & deep Learning model [1] and RNN model [3], but the weakness above has not been completely overcome. Although a fully connected neural network (FCNN) can be used to learn over numerical features, it easily leads to local optimization due to its complex structure [30].…”

“…Credit scoring, as the main method of personal credit reporting, is an automatic assessment tool for rejecting or accepting loan requests. It distinguishes the borrower into two types of good and poor credit based on the characteristics of personal information, and then decides whether to provide loan [1]. Credit scoring method has been widely used in tradi-tional financial institutions for many years.…”

“…According to the current research, the main methods of credit scoring are based on data mining and machine learning [1]- [7]. There are still two limitations.…”

A neural‐network‐based decision‐making model in the peer‐to‐peer lending market

“…They focused on the expected profitability of investing in P2P loans using the well‐known financial variable called the internal rate of return (IRR), and the results showed promising IRR. Many studies have used IRR to evaluate the profitability of loans in this market (Bastani, Asgari, & Namavari, 2019; Cho, Chang, & Song, 2019), but IRR can scarcely be used to rate mutually exclusive loans (Magni, 2013). Therefore, investigating models that completely consider the special requirements of loan evaluation in P2P lending is crucial.…”