Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection
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 attentio...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2021/6077540 |
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| _version_ | 1850214692184653824 |
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| author | Jian Yang Zixin Tang Zhenkai Guan Wenjia Hua Mingyu Wei Chunjie Wang Chenglong Gu |
| author_facet | Jian Yang Zixin Tang Zhenkai Guan Wenjia Hua Mingyu Wei Chunjie Wang Chenglong Gu |
| author_sort | Jian Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7ff7cb2f532644e78d019afe6dfbc34e |
| institution | OA Journals |
| issn | 1607-887X |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-7ff7cb2f532644e78d019afe6dfbc34e2025-08-20T02:08:49ZengWileyDiscrete Dynamics in Nature and Society1607-887X2021-01-01202110.1155/2021/6077540Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud DetectionJian Yang0Zixin Tang1Zhenkai Guan2Wenjia Hua3Mingyu Wei4Chunjie Wang5Chenglong Gu6School of ComputerSchool of ComputerSchool of ComputerSchool of ComputerSchool of ComputerSchool of ComputerSchool of ComputerFraud 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.http://dx.doi.org/10.1155/2021/6077540 |
| spellingShingle | Jian Yang Zixin Tang Zhenkai Guan Wenjia Hua Mingyu Wei Chunjie Wang Chenglong Gu Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection Discrete Dynamics in Nature and Society |
| title | Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection |
| title_full | Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection |
| title_fullStr | Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection |
| title_full_unstemmed | Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection |
| title_short | Automatic Feature Engineering-Based Optimization Method for Car Loan Fraud Detection |
| title_sort | automatic feature engineering based optimization method for car loan fraud detection |
| url | http://dx.doi.org/10.1155/2021/6077540 |
| work_keys_str_mv | AT jianyang automaticfeatureengineeringbasedoptimizationmethodforcarloanfrauddetection AT zixintang automaticfeatureengineeringbasedoptimizationmethodforcarloanfrauddetection AT zhenkaiguan automaticfeatureengineeringbasedoptimizationmethodforcarloanfrauddetection AT wenjiahua automaticfeatureengineeringbasedoptimizationmethodforcarloanfrauddetection AT mingyuwei automaticfeatureengineeringbasedoptimizationmethodforcarloanfrauddetection AT chunjiewang automaticfeatureengineeringbasedoptimizationmethodforcarloanfrauddetection AT chenglonggu automaticfeatureengineeringbasedoptimizationmethodforcarloanfrauddetection |