Anti-money laundering

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Anti-money laundering

Make use of the technical advantages of big data analysis, machine learning and rule engine to build a full stack of innovative anti money laundering monitoring and analysis technical framework, serve the fields of financial sanctions compliance, anti money laundering supervision, foreign exchange control and risk monitoring, and fully meet the compliance requirements of regulators.
Customer identification

Through customer basic information, related product information, related person information, internal and external label information, effectively identify customers and gain insight into potential risks in advance.

Anti-money laundering rating

The rating model is composed of feature weight, score and classification coefficient. The online merchants or individuals are sorted and stratified by quantitative scoring method, and continuously monitored through dynamic rating and manual rating.

Regulatory submission

Conduct risk disposal for abnormal and suspicious transactions, generate reports and messages, and submit large and suspicious transaction reports to the anti money laundering monitoring center of the central bank.

Abnormal and suspicious transaction monitoring

Carry out monitoring on the list related to crime and terrorism, identify abnormal and suspicious transactions through rules and strategies, intelligent model of anti money laundering scene and spectrum analysis, and deal with risks.

Committed to monitoring technological innovation

New technical means such as supervision technology formed by the organic combination of supervision and technology can play an important role in anti money laundering supervision and improve the effectiveness of fund monitoring

Suspicious group mining and analysis

Select the capital transactions within the time period, establish the customer capital transaction relationship network, divide the network diagram into communities with a directed graph, and build groups with anti money laundering discrimination, which has high business value for the identification of anti money laundering groups. Carry out risk rating, sequencing and in-depth analysis on the groups, and the inspection department pays attention to the investigation of communities with high risk.

Anti-money laundering

Conduct risk disposal for abnormal and suspicious transactions, generate reports and messages, and submit large and suspicious transaction reports to the anti money laundering monitoring center of the central bank.

  • Fully meet the anti-money laundering requirements of regulators

  • Specialized customer rating model

  • Specialized anti-money laundering model

  • Reduce the cumbersome reporting of anti money laundering

  • High throughput to meet the future growth

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Abnormal and suspicious transaction monitoring

Carry out monitoring on the list related to crime and terrorism, identify abnormal and suspicious transactions through rules and strategies, intelligent model of anti money laundering scene and spectrum analysis, and deal with risks.

Regulatory submission

Conduct risk disposal for abnormal and suspicious transactions, generate reports and messages, and submit large and suspicious transaction reports to the anti money laundering monitoring center of the central bank.

Anti-money laundering rating

The rating model is composed of feature weight, score and classification coefficient. The online merchants or individuals are sorted and stratified by quantitative scoring method, and continuously monitored through dynamic rating and manual rating.

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Anti-money laundering

Make use of the technical advantages of big data analysis, machine learning and rule engine to build a full stack of innovative anti money laundering monitoring and analysis technical framework, serve the fields of financial sanctions compliance, anti money laundering supervision, foreign exchange control and risk monitoring, and fully meet the compliance requirements of regulators.

  • Multidimensional customer identification

  • Multiple sanctions for screening and interception

  • Accurate and suspicious transaction positioning

  • Institutional money laundering risk rating

  • Data submission of banking regulatory institutions

  • Committed to monitoring technological innovation