Recognition method of accounting fraud risk based on financial knowledge graph

Qiang CHEN; Shiya DAI

doi:10.11959/j.issn.2096-0271.2021029

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Recognition method of accounting fraud risk based on financial knowledge graph

TOPIC：BIG DATA BASED KNOWLEDGE GRAPH AND ITS APPLICATIONS | 更新时间：2024-06-03

- Recognition method of accounting fraud risk based on financial knowledge graph
- Big data research Vol. 7, Issue 3, Pages: 2021029(2021)
- 作者机构：
  
  1. 兴业银行信息科技部，上海 201201
  2. 蚂蚁科技国际事业群数据算法技术部，上海 200120
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-0271.2021029
  CLC： TP311，F832
- Online First：2021-05，
  
  Published：15 May 2021
- 稿件说明：
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Qiang CHEN, Shiya DAI. Recognition method of accounting fraud risk based on financial knowledge graph[J]. Big data research, 2021, 7(3): 2021029.
DOI：

Qiang CHEN, Shiya DAI. Recognition method of accounting fraud risk based on financial knowledge graph[J]. Big data research, 2021, 7(3): 2021029. DOI： 10.11959/j.issn.2096-0271.2021029.

摘要

针对商业银行会计案件日益复杂且频发的问题，将会计案防领域的行业知识与金融知识图谱技术结合，以更精准地识别与防范商业银行会计风险。采用图分析、图挖掘等技术，提取深层关联风险特征，并与行业经验知识相结合，构建了249条单点规则及425条组合规则，形成了丰富、可灵活配置的反欺诈策略体系。将该智能化反欺诈方法应用于银行活期账户的风险排查，与传统规则策略相比，识别精准度大幅提升，且对于筛选出的高度可疑账户，识别精准度达到85%左右，极大提升了会计案件核查的效率。

Abstract

Since the accounting risk events exhibit complexity increasingly and occur frequently

a method merged by industrial knowledge and financial knowledge graph was proposed to recognize and prevent commercial bank's accounting risk more precisely.Based on the financial knowledge graph of account transaction

deep graph connected risk features were extracted via various graph analysis and mining technologies.Combining the graph features with industrial knowledge

249 single rules and 425 assembled rules were constructed to form a more affluent and flexibly configurable anti-fraud strategy system

which was then applied to verify commercial bank's current accounts and select the high suspicious ones.The experimental results show that the risk recognition accuracy rate of the intelligent strategy is much higher than the traditional one and reaches up to 85% above

which significantly promotes the efficiency of the accounting risk verification.

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references

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