Risk assessment method and device based on XGBoost and electronic equipment
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A technology for risk assessment and risk, applied in digital data processing, payment system, agreement authorization, etc., to avoid overfitting, improve model accuracy, and improve the effect of evaluation
Pending Publication Date: 2021-02-09
SHANGHAI QIYUE INFORMATION TECH CO LTD
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Embodiment 1
[0047] Below, will refer to Figure 1 to Figure 3 An embodiment of the XGBoost-based risk assessment method of the present invention is described.
[0048] figure 1 It is a flowchart of the risk assessment method based on XGBoost of the present invention. Such as figure 1 As shown, a risk assessment method, the method includes the following steps.
[0049] Step S101, acquiring historical sample data, and determining positive and negative samples. The historical sample data includes user social data, user associated person data and fraud data.
[0050] Step S102, based on the acquired historical sample data, extract risk characteristic variables, and establish a training data set and a verification data set.
[0051] Step S103, screening risk characteristic variables according to screening rules.
[0052] Step S104, using the XGBoost algorithm to build a risk identification model and use the training data set for training.
[0053] Step S105, acquire the user social data ...
Embodiment 2
[0096] An apparatus embodiment of the present invention is described below, and the apparatus can be used to execute the method embodiment of the present invention. The details described in the device embodiments of the present invention should be regarded as supplements to the above method embodiments; details not disclosed in the device embodiments of the present invention can be implemented by referring to the above method embodiments.
[0097] refer to Figure 4 , Figure 5 and Figure 6 , the present invention also provides a risk assessment device 400 based on XGBoost, including: a data acquisition module 401, used to acquire historical sample data, determine positive and negative samples, the historical sample data includes user social data, user associated person data and fraud data; extraction module 402, based on the acquired historical sample data, extracts risk characteristic variables, and establishes training data set and verification data set; screening module...
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Abstract
The invention provides a risk assessment method and device based on XGBoost and electronic equipment. The method comprises steps: acquiring historical sample data, and determining positive and negative samples, wherein the historical sample data comprises user social data, user associated person data and fraud data; based on the obtained historical sample data, extracting risk characteristic variables, and establishing a training data set and a verification data set; screening risk characteristic variables according to a screening rule; constructing a risk identification model by using an XGBoost algorithm, and performing training by using the training data set; obtaining user social data and user associated person data of a current user, and calculating a risk assessment value of the userby using the risk identification model; and judging whether to trigger an early warning mechanism or not based on the risk assessment value of the user. According to the method and device, the risk condition of the user can be accurately evaluated, the fraudulent user is effectively identified, the model precision is improved, over-fitting is avoided, and the evaluation effect is remarkably improved.
Description
technical field [0001] The present invention relates to the field of computer information processing, in particular to an XGBoost-based risk assessment method, device and electronic equipment. Background technique [0002] Risk control (referred to as risk control) means that risk managers take various measures and methods to eliminate or reduce the various possibilities of risk cases, or risk controllers reduce the losses caused by risk cases. Risk control is generally used in the financial industry, such as risk control for company transactions, business transactions or personal transactions. [0003] In the context of expanding business scale, diversification of fraud methods, and rising labor costs, existing fraud prevention methods tend to refine the anti-fraud management model, and establish intelligent anti-fraud management based on the fraud scoring model, forming a quantitative A business model combining analytical and qualitative analysis is imperative. Furthermo...
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