Knowledge Graph-based Financial Risk Audit System
Provides financial institutions with efficient regulatory compliance support
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Integrating big data distributed technologies, AI algorithm models, and knowledge graph-based exploratory business intelligence (BI), the product provides financial institutions with a complete set of solutions, including an off-site audit data middle office that aggregates multi-source heterogeneous data, an audit compliance model that combines AI technologies and financial know-how, a unified audit knowledge graph and a unified knowledge base for the financial sector, and a visualized analysis interface that features human-machine interaction.

Features and Advantages

A data model (schema) designed for audit purpose
A data model (schema) designed for audit purpose
Provides data familiar to auditors.
A comprehensive model knowledge base
A comprehensive model knowledge base
Lists various models of cases that violate laws or regulations for the convenience of auditors.
Exploratory business intelligence (BI) based on business scenarios
Exploratory business intelligence (BI) based on business scenarios
Offers exploratory BI to assist off-site auditors.
Mature experience in the industry
Mature experience in the industry
Supports an off-site audit system equipped with know-how required in the audit sector

Product Structure

Various Business Scenarios for Digitalization Construction

Risk warning
Risk warning
Concentrates on monitoring and early warning against post-loan risks.
Fraud prevention for financial institutions
Fraud prevention for financial institutions
Prevents fraud in the transaction, financing, and marketing processes.
Audit and accountability within financial institutions
Audit and accountability within financial institutions
Strengthens audit and accountability mechanisms for in-house employees, strictly preventing internal and external collusion.
Anti-money laundering for financial institutions
Anti-money laundering for financial institutions
Provides anti-money laundering solutions for large and suspicious transactions.

Related Case

基于知识图谱的智能审计项目
明略科技为客户银行基于全行全量数据构建成“企业、个人、机构、账户、交易、以及行为数据”,规模达十亿点百亿边的知识图谱数据库。通过采用复杂网络、图计算等大数据算法,实现海量结构化与非结构化数据的分析和探索。
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基于知识图谱的智能审计项目
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