Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection

Jian Yang,Zhenkai Guan,Chenglong Gu,Chunjie Wang,Wenjia Hua,Mingyu Wei,Zixin Tang,Gengxin Sun

doi:10.1155/2021/6077540

Abstract

Fraud detection is one of the core issues of loan risk control, which aims to detect fraudulent loan applications and safeguard the property of both individuals and organizations. Because of its close relevance to the security of financial operations, fraud detection has received widespread attention from industry. In recent years, with the rapid development of artificial intelligence technology, an automatic feature engineering method that can help to generate features has been applied to fraud detection with good results. However, in car loan fraud detection, the existing methods do not satisfy the requirements because of overreliance on behavioral features. To tackle this issue, this paper proposed an optimized deep feature synthesis (DFS) method in the automatic feature engineering scheme to improve the car loan fraud detection. Problems like feature dimension explosion, low interpretability, long training time, and low detection accuracy are solved by compressing abstract and uninterpretable features to limit the depth of DFS algorithm. Experiments are developed based on actual car loan credit database to evaluate the performance of the proposed scheme. Compared with traditional automatic feature engineering methods, the number of features and training time are reduced by 92.5% and 54.3%, respectively, whereas accuracy is improved by 23%. The experiment demonstrates that our scheme effectively improved the existing automatic feature engineering car loan fraud detection methods.

Highlights

Car loans, with the characteristics of low threshold, small amount, high liquidity, short cycle, and so forth, have become an important part of online loans
We can draw the following conclusions: (1) By comparing the receiver operating characteristic curve (ROC) curve and area under ROC curve (AUC) coefficient of all the groups, it is shown that the automatic feature group B performs better. 23% of the corresponding AUC value of group B is optimized compared with group A, and 25.5% optimized compared with the benchmark group
(2) Compared with the automatic feature group A, the automatic feature group B shortens the total construction time and training time by 54.3%. e number of generated features is reduced by 92.5%. e readability and interpretation ability are optimized

Summary

Introduction

With the characteristics of low threshold, small amount, high liquidity, short cycle, and so forth, have become an important part of online loans. The car loan business will certainly face the three following risks: fraud risk, credit risk, and postloan risk. Fraud risk refers to whether the car loan business carried out by the platform has the possibility of attracting fraud gangs to cheat loans. Credit risk refers to whether a single borrower who buys a car has repayment ability. Postloan risk refers to the ability of the platform to dispose of assets after being overdue. Us, in recent years, due to the continued increase in business volume, optimizing fraud detection, solving a series of problems in credit application fraud, financial intermediary identification, ganging monitoring, or early warning, and building an antifraud cloud platform by means of artificial intelligence methods to improve risk control capability have become a prevailing topic. Taha and Malebary [4] proposed a new OLightGBM method on the basis of LightGBM [5] algorithm, incorporating the Bayesbased hyperparameter optimization algorithm to achieve credit card fraud detection. e detection accuracy of this method reaches 98.40% on real datasets, which include both fraudulent transactions and legitimate transactions

Methods

Discussion

Conclusion