The challenge seeks to address the automation of the alert review and adjudication process for alerts generated from screening devices such as name screening and payments screening alerts.

The solution should address both the alert types i.e. Name Screening Alerts and Payment Screening Alerts.

1. Machine Learning-Powered Decision Making:

- The solution must incorporate ‘machine learning’ models that learn from historical alert review outcomes and decisions made by the first line of defence team.

- Over time, the model should become proficient in distinguishing between true positives (alerts requiring action) and false positives (benign alerts that can be dismissed).

2. Data Sources and Historical Training:

- The solution needs to be trained on past adjudication data, where alerts were manually classified by the 1LoD team.

- Data inputs should include:

- The nature of the alert (e.g., exact or fuzzy match for name, specific payment details).

- Historical decisions (whether an alert was escalated, dismissed, or resulted in further action).

- Any additional factors influencing the decision (such as context or external information used by the 1LoD team).

3. Name and Payment Matching Algorithms:

- Name Screening: The solution should use sophisticated matching algorithms (including fuzzy matching) to account for misspellings, aliases, transliterations, and name variations (common in sanctions screening).

- Payment Screening: For payments, the solution should detect patterns, such as:

- Unusual transaction amounts.

- Transfers to/from high-risk jurisdictions.

- Repeated small-value transactions that may be indicators of suspicious activity (structuring).

4. Workflow Automation and Integration:

- The solution should integrate seamlessly with existing screening systems

- It should provide a streamlined workflow, allowing human analysts to review and confirm machine recommendations efficiently.

- The ability to automate specific decisions (e.g., dismissing obvious false positives) is critical to reduce manual intervention.

5. Continuous Learning and Feedback Loop:

- The solution must have a ‘feedback mechanism’ where analysts' final decisions on alerts (whether overridden or confirmed by the model) are fed back into the machine learning system to continuously improve its accuracy over time.

6. Auditability and Compliance:

- Since this solution deals with compliance, it must be fully auditable. Every decision made by the system (whether automated or flagged for review) should be logged, with a clear rationale that can be reviewed by auditors or regulators.

- It must comply with relevant **regulatory standards jurisdiction, ensuring data privacy and accuracy in decision making.

7. Scalability and Customization:

- The solution should be scalable to handle varying alert volumes, especially in global financial institutions where transaction volumes can fluctuate.

- There must be the ability to ‘customize’ rules and thresholds based on risk appetite, regulatory requirements, or business policies specific to certain regions, sectors, or client types.