A Suspicious Transaction Monitoring System (STMS) is a software-based system used by banks and other financial
institutions to monitor and identify potentially suspicious transactions. The system uses algorithms and rules
to analyze transactional data in real-time or in batches to identify transactions that may be indicative of money laundering,
terrorist financing, or other financial crimes.
The Suspicious Transaction Monitoring System (STMS) is very important to bank compliance department
as the STMS is typically used by banks as part of their compliance efforts to identify and prevent money laundering and other illicit financial activities.
1. What is suspecious transaction monitoring system (STMS)?
The STMS typically uses a range of data sources to analyze transactions,
including customer data, account data, transaction data, and other sources of external data such as watch lists or other risk indicators.
The system compares these data sources against pre-defined rules, models, or scenarios to detect transactions that are unusual, inconsistent,
or non-compliant with regulations.
When a potentially suspicious transaction is identified, the STMS generates an alert that is sent to a team of analysts or
investigators within the bank for further investigation. These alerts may include information on the transaction and its associated parties,
as well as any risk indicators or red flags that were identified by the system.
The STMS is an important component of a bank’s anti-money laundering and counter-terrorist financing program.
By monitoring and identifying potentially suspicious transactions, the system helps banks to comply with regulatory requirements and prevent financial crime.
It also helps to protect the bank’s reputation and mitigate the risk of financial losses or penalties associated with non-compliance.
2. What models or algorithms usually be used in Suspicious Transaction Monitoring System?
The specific models or algorithms used in a Suspicious Transaction Monitoring System (STMS) can vary
depending on the institution and the specific needs of their monitoring program. However,
there are several commonly used techniques that can be applied to STMS.
2.1 Rule-based systems
These systems use a set of pre-defined rules to identify suspicious transactions.
The rules may be based on various criteria, such as transaction amounts, transaction patterns,
customer behavior, or other risk factors. When a transaction meets one or more of the pre-defined rules,
an alert is generated for further investigation.
2.2 Machine learning models
Machine learning techniques, such as neural networks or decision trees, can be used to identify patterns of behavior that may
indicate suspicious activity. These models are trained on historical transactional data to identify common
features or behaviors that are associated with suspicious activity. They can then be applied to new transactional data to
identify transactions that may be indicative of suspicious activity.
2.3 Network analysis
Network analysis techniques can be used to identify relationships between customers and other entities,
such as third-party vendors or other financial institutions. These relationships can be used to identify
suspicious activity that may involve multiple parties or transactions across different accounts.
2.4 Natural Language Processing (NLP)
NLP can be used to analyze the content of transaction descriptions or messages for keywords or phrases that may indicate suspicious activity.
3.1 Actimize
Actimize is a comprehensive STMS platform that uses a range of techniques,
including machine learning, to identify potentially suspicious activity. It can be customized to meet the specific needs
of an institution and is used by many large financial institutions.
3.2 FICO Falcon
FICO Falcon is another popular STMS platform that uses rule-based systems and machine learning to identify potentially suspicious activity.
It is used by many large financial institutions and can be customized to meet the specific needs of an institution.
3.3 SAS Anti-Money Laundering
SAS Anti-Money Laundering is a comprehensive STMS platform that uses rule-based systems,
statistical analysis, and machine learning to identify potentially suspicious activity.
It can be customized to meet the specific needs of an institution and is used by many large financial institutions.
Open-source STMS platforms such as Apache NiFi, Apache Storm, and Apache Flink can be used to build custom STMS solutions using a range of techniques and technologies.