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Financial abnormity detection method and device based on multi-label learning

An anomaly detection and multi-label technology, applied in the computer field, can solve problems such as low accuracy, few data samples, and inability to detect financial whitewashing methods, and achieve the effects of improving efficiency, improving accuracy, and improving detection accuracy

Pending Publication Date: 2020-10-16
GF SECURITIES CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

This technology uses Multi-Label Learning (ML) techniques to detect potential bankruptcies caused by economic factors like credit card fraud. By generating representative vectors representing different attributes associated with transactions within one country's borders, this technique helps identify deviations between these two countries without relying solely upon previous patterns. Additionally, it allows users to compare their performance against past records to see if they have been affected beforehand. Overall, MLA ensures accurate prediction about future events related to banks while reducing manual effort required over time.

Problems solved by technology

This patented technical problem addressed in the patents relates to improving understanding of finances related to firms or smaller entities (such as banks) due to their lack of visibility over vast amounts of money being leaked around them. Existing models like machine learning algorithms cannot provide accurate analysis without sacrifices significant effort from human judges who manually inspect these documents.

Method used

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  • Financial abnormity detection method and device based on multi-label learning
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  • Financial abnormity detection method and device based on multi-label learning

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Embodiment Construction

[0047] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0048] see figure 1 , is a schematic flowchart of an embodiment of the multi-label learning-based financial anomaly detection method provided by the present invention. Such as figure 1 The shown method includes step 101 to step 105, and each step is specifically as follows:

[0049] Step 101: Obtain the financial information of several enterprises, and generate eigenvector samples for each enterprise according to the financial information of each enterprise; wher...

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Abstract

The invention discloses a financial abnormity detection method and device based on multi-label learning. The method comprises the steps: generating a feature vector sample of each enterprise accordingto the financial information of an existing enterprise, carrying out the sample balance according to a preset sampling parameter, and obtaining a training sample set; labeling the training sample setaccording to a plurality of labels obtained according to historical responsibility asking data, and constructing a financial abnormity detection model based on a multi-label learning algorithm; and finally, obtaining financial information of a to-be-detected enterprise, constructing a sample input vector, and inputting the sample input vector into the financial abnormity detection model to obtaina detection result. By adopting the technical scheme of the invention, the problem of low accuracy caused by few data samples and incapability of detecting financial whitewash means can be solved, and the detection accuracy is improved.

Description

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Claims

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Application Information

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Owner GF SECURITIES CO LTD
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