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Anti-money Laundering (AML)

Anti-money laundering (AML) refers to a set of procedures, laws or regulations designed to stop the practice of generating income through illegal actions.


Necessity of fighting money laundering

With the increasing occurrence of Infractions, damage to organizations and institutions also increases exponentially and if the appropriate solutions are not taken to combat these offenders, losses cause bankruptcy of the industry.


The effect of money laundering on economy

Money laundering is a financial crime which has a negative effect on economic growth and development. Among the negative effects of money laundering, the following can be noted:

  • Deterioration of financial markets
  • Bankruptcy of the private sector
  • Increasing the risk of privatization


What is SON’s strategy to fight money laundering?

SON Anti-Money Laundering helps you take a risk-based approach to monitoring transactions for illicit activity to comply with anti-money laundering (AML) and the Central Bank of the Islamic Republic of Iran financing (CBI) regulations. It uses a combination of behavioral and peer-based analytic techniques that lead to improved detection accuracy. SON Anti-Money Laundering allows financial institutions to safeguard their reputations and avoid fines and penalties associated with noncompliance. Its standard analytic routines can monitor for known money laundering risks, and behavioral analytic monitors for abnormal behaviors.

Anti-money laundering system for analyzing financial data requires a centralized data warehouse, so it is closely resembled to business intelligence systems. The cornerstone of both systems are designing and building data warehouses, proper ETL mechanisms, design and implementation of data analysis procedures and user interfaces to display specific results to users.

Benefits of implementation of AML

· Monitor more transactions in less time

· Ability to visualize emerging risks

· Reduce the offenses incurred by the organization as a result of violations

· Reducing the manpower required to monitor, detect and investigate suspected cases


· Improve alert quality and accuracy

· Suspicious activity monitoring and reporting

· Eliminate human error in preparation of reports

· Generate required reports