Neural Network Loan Fraud Detection Model Training
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Solution Overview
Problem
Manual credit checking for loan fraud detection is inefficient and inaccurate, leading to high human costs and difficulty in integrating bank statement data with personal information to identify fraudulent users.
Innovation Solution
A training method and apparatus for a loan fraud detection model using neural networks that acquire identity and bank statement information, transform feature vectors, and concatenate behavior patterns with statement features to generate target vectors for fraud detection, improving recognition efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checking is used to verify user credentials and bank statements, then the lending platform can identify fraudulent users through expert judgment, but the checking efficiency is low and human costs are high
Solution Approach 1:
The patent replaces the mechanical manual checking system with an automated neural network system. The neural network automatically processes user identity information and bank statement data, performing fraud detection without human intervention. This substitution maintains detection accuracy while dramatically improving checking efficiency and reducing human costs.
Solution Approach 2:
The neural network system performs self-service by automatically learning from training data and making fraud detection decisions independently. The system autonomously processes loan applications, analyzes bank statements, and identifies fraudulent users without requiring manual expert judgment for each case, thereby improving productivity while maintaining precision.
2Measurement precision
If manual checking is used to integrate bank statement data and personal information, then expert judgment can make overall correct conclusions, but the complexity of handling large volumes of information makes it difficult to achieve accurate integration
Solution Approach 1:
The patent replaces complex manual information integration with an automated neural network system. The neural network automatically processes and integrates multiple data sources including bank statements and personal information, handling the complexity of data fusion without requiring manual expert judgment, thereby improving integration accuracy while managing processing complexity.
Solution Approach 2:
The neural network system performs multiple functions including data cleaning, feature extraction, pattern recognition, and fraud detection in a single integrated process. This multi-functionality allows the system to handle various types of information (bank statements, personal data, transaction records) uniformly, improving integration accuracy without increasing operational complexity.
3Measurement precision
If more manual reviewers are deployed to improve fraud detection accuracy, then detection precision can be enhanced, but the operational costs and processing time increase
Solution Approach 1:
The patent replaces multiple manual reviewers with a single automated neural network system that processes applications rapidly. The neural network achieves fraud detection precision comparable to or exceeding manual review while dramatically reducing processing time, as it can analyze multiple data points simultaneously without the sequential bottlenecks of human review.
Solution Approach 2:
The neural network performs preliminary analysis of all loan applications automatically before any human review is needed. By pre-processing and filtering applications through the trained model, the system identifies obvious fraud cases quickly and prepares structured recommendations, reducing the time required for subsequent review while maintaining high detection precision.
Data Source
AI summary
Training method and apparatus for loan fraud detection model and a computer device are provided, wherein the training method for loan fraud detection model includes: acquiring identity information and user's bank statement information of a plurality of sample users, and fraud label information corresponding to each user; constructing an identity feature vector and a behavior pattern vector according to the identity information; constructing a statement feature vector according to the behavior pattern vector, a second vector transformation matrix and the user's bank statement information; generating a target feature vector according to the behavior pattern vector and the statement feature vector; feeding a target neural network with the target feature vector to acquire a fraud detection result of the target feature vector; and training the target neural network, the first vector transformation matrix and the second vector transformation matrix to obtain a loan fraud detection model.