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EEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 30, NO. 5, MAY 2019 1565

A Deep Learning Approach to Competing Risks Representation in Peer-to-Peer Lending

Fei Tan , Student Member, IEEE, Xiurui Hou, Jie Zhang, Student Member, IEEE,

Zhi Wei , Senior Member, IEEE, and Zhenyu Yan

Abstract— Online peer-to-peer (P2P) lending is expected to benefit both investors and borrowers due to their low transaction cost and the elimination of expensive intermediaries. From the lenders’ perspective, maximizing their return on investment is an ultimate goal during their decision-making procedure. In this paper, we explore and address a fundamental problem underlying such a goal: how to represent the two competing risks, charge-off and prepayment, in funded loans. We propose to model both potential risks simultaneously, which remains largely unexplored until now. We first develop a hierarchical grading framework to integrate two risks of loans both qualitatively and quantitatively. Afterward, we introduce an end-to-end deep learning approach to solve this problem by breaking it down into multiple binary classification subproblems that are amenable to both feature representation and risks learning. Particularly, we leverage deep neural networks to jointly solve these subtasks, which leads to the in-depth exploration of the interaction involved in these tasks. To the best of our knowledge, this is the first attempt to characterize competing risks for loans in P2P lending via deep neural networks. The comprehensive experiments on real-world loan data show that our methodology is able to achieve an appealing investment performance by modeling the competition within and between risks explicitly and properly. The feature analysis based on saliency maps provides useful insights into payment dynamics of loans for potential investors intuitively.

Index Terms— Competing risks, deep neural networks, peer-to-peer (P2P) lending, return on investment (ROI).

I. INTRODUCTION

PEER-TO-PEER (P2P) lending has become a fast-growingnew channel of financing over the past decade. Quite a few P2P platforms have been developed, includ- ing Lending Club (LC) (www.lendingclub.com), Prosper (www.prosper.com), Yirendai (https://www.yirendai.com), and Zopa (www.zopa.com). Connecting borrowers with investors directly using technology, those P2P platforms claim to operate at a lower cost than traditional bank loan programs, passing the savings on to borrowers in the form of lower rates and to investors in the form of solid returns. Such credit marketplaces

Manuscript received September 28, 2017; revised April 6, 2018 and August 15, 2018; accepted September 2, 2018. Date of publication October 10, 2018; date of current version April 16, 2019. (Corresponding author: Zhi Wei.)

F. Tan, X. Hou, and Z. Wei are with the Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102 USA (e-mail: ft54@njit.edu; xh256@njit.edu; zhiwei@njit.edu).

J. Zhang and Z. Yan are with Adobe Systems, San Jose, CA 95110 USA (e-mail: jiezhan@adobe.com; wyan@adobe.com).

Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TNNLS.2018.2870573

have thus attracted a lot of lenders (investors) and borrowers and result into a large amount of investments. For example, as of June 30, 2018, the total loan issued by LC (the world’s largest P2P lending platform) has exceeded U.S. $38 billion.1

For P2P lending, three major participants are involved in the transaction procedure: the lending platform, lenders, and borrowers. Lenders and borrowers interact with each other directly on the lending platform. Using LC for example, we briefly introduce the working mechanism of P2P lending. Other P2P lending platforms are somewhat similar. In LC, a borrower (sometimes with coborrowers) is supposed to provide his or her detailed profile (e.g., annual income and housing status) and loan information while creating a listing to solicit investments from lenders. After receiving the listing, the platform verifies borrowers’ profile (optional), evaluates their credit, and then assigns a certain grade or subgrade to the listed loan for lenders’ reference. If the listed loan gets fully funded by the expiration date, it will be issued by the platform or otherwise revoked. Afterward, the investors can secure interests and the platform charges service fees from borrowers’ monthly payments. Like in most conventional bank loan programs, borrowers may prepay their loans at any time, in whole or in part, without penalty; lenders will then receive pro-rata share of the payment. A loan can also become “charged off” when there is no longer a reasonable expectation of further payments.