Anti-Money Laundering Screening (AML)
Discover simple way to identify suspicious transactions with this Decision Flow based on their characteristics.
DecisionRules
About the example:
This Decision Flow serves for real-time identification of suspicious transactions during transaction processing in order to reach compliance with Anti Money Laundering legislation.
Solution components:
- a Decision Table named Risk Scoring assigns score points to transactions based on characteristics such as transaction type and value, past transactions, customers characteristics, etc.
- a Decision Table named Decision Thresholds evaluates the total score against the defined thresholds and decides on the transaction authorization (automatic, manual, escalation)
- a Decision Flow orchestrates these two Decision tables: executes calculation of score points using the Risk Scoring table, sums the score, passes the total score to the Decision Thresholds table and assigns its output the the Decision Flow output.
Use this Decision Flow, adjust it to match your needs and simply call from your transaction processing system using a REST API
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