Company Name:
TESS INNOVATION SDN BHD (200001006008 (508613-A))
Sector
Digital Finance
Tech Enabler
Artificial Intelligence (AI) and/or Big Data Analytics (BDA)
Overview:
Our cutting-edge “AI Segmentation” solution tackled a critical challenge for BSN's Anti-Money Laundering (AML) monitoring. Previously, BSN struggled with high volumes of false positive red flags, overlapping and ineffective rule monitoring By integrating AI Segmentation, we helped BSN achieve to focuses on relevant cases, minimizing wasted investigations while streamlined rule monitoring. This groundbreaking solution empowers BSN with a more effective and efficient AML monitoring system.
Pain Points:
Current AML systems can pose significant challenges for financial institutions (FIs) due to their impact on compliance officer productivity and work management. These challenges include:
1. Generate high number of false positive, leading to a significant investment in resources for unnecessary investigation.
2. Fail to identify true positive matches, allowing potential money launderers to evade detection
Solution/Services:
The "AI Segmentation" solution
Leverages machine learning to automatically group customers based on their historical transaction patterns.
Key Features and Functionalities
- Tailor AML monitoring rules to specific customer segments: By understanding customer behaviour, the AI/ML can create targeted rules that are more likely to identify true positive matches (suspicious activity) and significantly reduce false positives (unnecessary alerts).
- Focus resources on high-risk customers: Grouping customers based on cluster allows compliance officers to prioritize investigations and dedicate resources to potentially suspicious activity.
- Segment the customer base automatically: Leverage machine learning to group customers based on their unique transaction behaviour patterns.
- Gain deeper insights: Analyse red flags, clusters, and rule thresholds to identify trends and patterns within each customer segment.
- Optimize AML rule: Based on the analysis, the system recommends the most effective rule scenarios to implement, ensuring a balance between catching suspicious activity and minimizing false positives
Implementation:
- Seamlessly integrates with the existing AML system and database using API. This allows:.
- Automated data extraction that it needs for segmentation, eliminating manual data preparation and ensuring efficiency.
- Actionable Insights where data is then processed and presented within the application for ease of user analysis. This empowers compliance officers to view and analyse segmentation results to understand the customer cluster profile and make well-informed decisions about adjusting rule monitoring thresholds for each segment based by the AI recommendation.
- The solution leverage on pre-built functionalities and avoiding complex customization allowing for rapid implementation at BSN and other financial institution.
Benefit:
- Reduced Alert Fatigue: One specific rule scenario saw a 90% reduction in generated alerts. This significantly minimizes wasted time investigating false positives and allows compliance officers to focus on truly suspicious activity.
- Enhanced Detection: The capture rate of suspicious transactions has increased by 10% compared to the previous rule configured. This improvement ensures that BSN doesn't miss potentially harmful activity.
- Boosted productivity: By streamlining case investigations and minimizing false positives making the AI Segmentation empowers BSN to achieve greater efficiency. Compliance officers can now dedicate more time to high-priority tasks, ultimately reducing resource costs.