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

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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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.

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