Account risk evaluation method based on geographic position

Abstract

The invention provides an account risk evaluation method based on a geographic position, and the method comprises the steps: S1, storing an account profile in an account database, and enabling the account profile to comprise data related to a transaction account, the account profile at least comprises a first profile rule associated with a first geographic position and a second profile rule associated with a second geographic position, and the second geographic position is different from the first geographic position; s2, receiving, by the receiving device, an authorization request involving a first payment transaction associated with a related transaction account. According to the account risk evaluation method based on the geographic position, the account risk is judged according to the geographic position migration, so that the account with the risk is accurately identified and locked, and the loss caused by the fact that the account is utilized by a hacker gang is prevented.

Description

technical field [0001] The invention relates to the technical field of risk assessment, in particular to an account risk assessment method based on geographic location. Background technique [0002] As the use of payment cards and other payment instruments associated with transaction accounts increases, so does the volume of often successful fraud attempts, and financial institutions, payment networks, merchants, and consumers are constantly trying to develop new ways to prevent transactions Fraud on Account. [0003] The rule-based method strongly relies on people's business understanding. With massive data, one-sided understanding is likely to occur. Without considering the dynamic changes of the business, some possible risks will be missed, resulting in inaccurate scoring results. [0004] Therefore, it is necessary to provide a geographic location-based account risk assessment method to solve the above technical problems. Contents of the invention [0005] In order t...

AI Technical Summary

Problems solved by technology

[0003] The rule-based method strongly relies on people's business understanding. With massive data, one-sided understanding is likely to occur. Without considering the dynamic changes of the business, some possible risks will be missed, resulting in inaccurate scoring results.