Introduction
To enhance Abrigo's BSA/AML software for better detection of emerging financial crime patterns, we need to take a comprehensive approach that considers user needs, technological advancements, and evolving regulatory requirements. I'll outline a strategy to improve the software's capabilities, focusing on key areas such as machine learning, data integration, and user experience.
Step 1
Clarifying Questions (5 mins)
Why it matters: Determines the level of expertise we should assume and the specific use cases to prioritize. Expected answer: Primarily used by compliance officers and analysts in banks and credit unions. Impact on approach: Would focus on advanced features for experienced users vs. simplifying for a broader audience.
Why it matters: Helps understand the agility of the current system and where improvements might be needed. Expected answer: Quarterly updates with some manual input required. Impact on approach: Would prioritize automating and accelerating the update process if it's currently slow.
Why it matters: Identifies a potential key area for improvement that directly impacts user efficiency and regulatory compliance. Expected answer: False positive rate around 95%, slightly above industry average. Impact on approach: Would focus heavily on reducing false positives if this is indeed a pain point.
Why it matters: Helps identify areas where we need to catch up or where we can differentiate. Expected answer: Mid-tier player with strengths in user interface but lagging in advanced analytics. Impact on approach: Would emphasize improving analytics capabilities while maintaining UI advantage.
I'd like to take a quick moment to organize my thoughts before we move on to the next section. This will help me structure our discussion more effectively.
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