Case Study: Hampshire Trust Bank (HTB)
HTB was looking for a transaction monitoring and screening solution that would meet the needs of its growing customer base while safeguarding the bank’s reputation.
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HTB was looking for a transaction monitoring and screening solution that would meet the needs of its growing customer base while safeguarding the bank’s reputation.
The ComplyAdvantage API enables you to integrate your systems with our services to automate many of the functions available through the web user interface. Our API follows the REST convention and accepts and returns JSON data. View our API docs.
The transaction monitoring solution uses an advanced rules-based system to evaluate the AML/CTF risk of a transaction in real-time. These rules include:
– Simple rules: Rules that perform a check on the data within a transaction
– Aggregate Rules: Rules that track activity over multiple transactions (e.g. velocity rules)
– Behavioral Rules: Rules that compare with past activity (e.g. average transaction amount)
– Risk Pattern Rules: Rules that cross reference activity against a known AML/CTF risk pattern
These rules are created and deployed during the implementation of your solution as this allows <<Customer>> to concentrate on the areas that will benefit your business the most (i.e. resolution of cases)
It is possible to select and export transaction data as a CSV file to be used outside of the ComplyAdvantage platform as needed. This contains all input data as well as additional output such as rule and alert information from the system.
Rules are completely customizable. Our transaction monitoring system can run on any data that you provide to us (data that is captured within a transaction). This not only allows the engine to detect suspicious scenarios specific to bankers banks, but also allows the tool to speak in your language, using your own variables, codes, definitions, etc. that are unique to you.
The ComplyAdvantage transaction monitoring solution is a rules-based, data-agnostic tool that can detect the types of suspicious behaviors/scenarios that the customer defines. Generally speaking, regulators have not “signed-off” on AI and machine learning for ML/TF risk detection when it comes to transaction monitoring systems.
Having said that, ComplyAdvantage is anticipating this acceptance in the future and is, therefore, preparing the tools for this change to the regulatory landscape.
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