Resource data processing method and device

Abstract

One or more embodiments of the invention disclose a resource data processing method and device, which are used for solving the problems of low data clustering efficiency and low risk management efficiency in the prior art. The method comprises the steps of determining at least one resource segmentation value corresponding to a plurality of resource data based on segmentation positions of the plurality of resource data; and performing clustering processing on the plurality of resource data by using the resource segmentation values to obtain a plurality of resource clustering groups; according to a preset resource evaluation index, determining a target resource clustering group corresponding to the resource evaluation index from the plurality of resource clustering groups, wherein the resource evaluation indexes comprise risk evaluation parameters used for performing risk evaluation on the resource data; and determining a resource evaluation threshold corresponding to the resource data according to a target resource segmentation value corresponding to the target resource clustering group. The resource evaluation threshold is used for performing risk evaluation on the resource data.

Description

technical field [0001] This document relates to the technical field of data processing and risk assessment, in particular to a resource data processing method and device. Background technique [0002] There are three main types of merchants involved in the risk control scenario: direct-connected merchants, indirect-connected merchants, and mini-program merchants. The risk management of these merchant entities includes four main links: merchant admission, merchant risk identification, merchant risk operation, and merchant risk decision makers. [0003] In all aspects of merchant management, risk control operations, strategies, and models are based on various data of merchant entities. In fact, there are innumerable connections between different merchant entities (such as overlapping identities, capital exchange relationships, etc.) , media sharing relationship, geographical proximity, etc.), if it is possible to aggregate different subjects based on the connection between su...

AI Technical Summary

Problems solved by technology

The calculation of quantiles is very common in descriptive statistics. For example, compared with the average, the median will not be affected by outliers, but the calculation process of quantiles is more complicated, and all specific values ​​​​need to be retained. After sorting get the number in the middle position as the result

This complex quantile calculation method leads to a reduction in the efficiency of subject aggregation, as well as a reduction in the efficiency of subject management (such as subject risk prevention and control, etc.)

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