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Vehicle loan application fraud identification method and device

A recognition method and car loan technology, applied in character and pattern recognition, instruments, business, etc., can solve the problem of not having the risk of mining customer fraud, large solution space, increasing model training time, etc., to save audit labor costs and fraud. Disposal cost, improvement in prediction accuracy, and effect of reducing the number of solution spaces

Pending Publication Date: 2022-01-07
长安汽车金融有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The current mainstream fraud detection methods in the industry mainly rely on artificially developed rules and statistical models, which have the following defects and deficiencies: First, the rules rely heavily on experience and intuition. The development of R&D requires a large number of analysts, the R&D investment is huge and the results are uncertain; moreover, the rules are generally not universal, and often can only achieve the expected effect for data under specific conditions; in addition, the statistical model is based on statistical Theory, scoring customers from multiple dimensions, although the effect is better than the rules, but it is mainly used to evaluate the customer's repayment ability, and does not have the ability to tap customer fraud risk
The existing common parameter tuning methods include grid search method, random search method and Bayesian optimization method, but these methods require a large solution space, which increases the model training time and reduces the model training efficiency

Method used

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  • Vehicle loan application fraud identification method and device
  • Vehicle loan application fraud identification method and device
  • Vehicle loan application fraud identification method and device

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0035]This example provides a parameter tuning method for the xgboost model. The xgboost model has 11 hyperparameters, the hyperparameter n_estimators is the number of tree models, the hyperparameter max_depth is the maximum depth of the tree model, and the hyperparameter min_child_weight is the smallest sample weight among child nodes And, the hyperparameter gamma is the minimum value of the loss reduction required for leaf nodes to branch, the hyperparameter subsample is the sampling rate of each subtree sample, the hyperparameter colsample_bytree is the sampling rate of each subtree feature, and the hyperparameter reg_alphaL1 is the regularization weight. The parameter reg_lambdaL2 is the regularization weight, the hyperparameter max_delta_step is the maximum moving step, the hyperparameter scale_pos_weight is the positive sample weight, and the hyperparameter learning_rate is the learning rate.

[0036] Traditional parameter tuning methods such as grid search, random search...

Embodiment 2

[0041] The present invention is improved on the basis of Embodiment 1. Since there is mutual influence among the 11 hyperparameters, different parameter tuning sequences will affect the parameter tuning results. Usually, several hyperparameters that have the greatest impact on the model effect are n_estimators, max_depth , min_child_weight, amma, subsample, colsample_bytree, and learning_rate. In the field of auto finance anti-fraud, since fraud samples are usually very few, the problem of sample imbalance is faced. At this time, the values ​​of the two hyperparameters scale_pos_weight and max_delta_step play a significant role, so the present invention proposes to focus on adjusting The above 9 hyperparameters. After trial and error, this embodiment selects an optimal training sequence according to the characteristics of the xgboost algorithm, such as figure 1 shown.

[0042] In this embodiment, i=7, that is, seven adjustments are required to complete the adjustment of 11 h...

Embodiment 3

[0052] The present invention limits the candidate values ​​of hyperparameters on the basis of Embodiment 1 or 2. Each hyperparameter has a corresponding value range. The general value range of each parameter is that n_estimators is an integer and n_estimators∈[200,600 ], Max_depth is an integer and Max_depth∈[2,8], Min_child_weight is an integer and Min_child_weight∈[1,10], Gamma is a real number and Gamma∈[1e-2,1], Subsample is a real number and Subsample∈[0.5,1 ], Colsample_bytree is real and Colsample_bytree ∈ [0.5, 1], Reg_alpha is real and Reg_alpha ∈ [1e-2, 1], Reg_lambda is real and Reg_lambda ∈ [1e-2, 1], Max_delta_step is real and Max_delta_step ∈ [0 , 1], Scale_pos_weight is a real number and Scale_pos_weight ∈ [1, 100], Learning_rate is a real number and Learning_rate ∈ [1e-2, 0.5].

[0053] It can be seen that the value range of each hyperparameter is very large. According to its own characteristics and the characteristics of the xgboost algorithm, this embodiment ...

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Abstract

The invention provides a vehicle loan application fraud identification method and device. The method comprises the following steps: carrying out machine learning based on an xgboost model, providing a parameter adjustment method of the xgboost model, when fraud identification is carried out, firstly, acquiring data corresponding to multiple vehicle loan application services, and extracting feature variables and fraud marks from the data to construct a sample set; performing parameter adjustment on hyper-parameters of the xgboost model by using the sample set to determine an optimal value of each hyper-parameter, and training and testing the xgboost model subjected to parameter adjustment; and finally, performing data cleaning and feature variable extraction on corresponding data of a to-be-identified car loan application service, converting the data into qualified input of the xgboost model, and inputting the qualified input into the trained xgboost model to obtain a fraud prediction result. According to the method, the model training efficiency can be improved, the fraud prediction accuracy is improved, the purpose of accurately identifying fraudulent customers is achieved, and the auditing labor cost and the fraud disposal cost can be greatly saved.

Description

technical field [0001] The invention belongs to the technical field of anti-fraud of auto finance, and relates to a fraud identification method for an auto loan application and equipment for realizing the fraud identification method for an auto loan application. Background technique [0002] Auto consumer loans have the characteristics of high unit price and long repayment period. In the field of auto finance, the health of the business depends on the bad debt rate of the overall lending customers. Bad debts are divided into credit and fraud. Credit means that it is difficult to repay, and fraud means that there is no willingness to repay. Fraudulent bad debts will cause direct losses to the business. Therefore, the maturity of anti-fraud technology is directly related to the healthy development of business. [0003] The current mainstream fraud detection methods in the industry mainly rely on artificially developed rules and statistical models, which have the following def...

Claims

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Application Information

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IPC IPC(8): G06Q40/02G06Q30/00G06K9/62
CPCG06Q30/0185G06Q40/03G06F18/214Y02T10/40
Inventor 李志立曹家楷赵轩张胜庆
Owner 长安汽车金融有限公司