1 S A L E S @ A Y A S D I . C O M + 1 6 5 0 . 7 0 4 . 3 3 9 5 W W W . A Y A S D I . C O M SensaAMLTM Retail Banking Case Study New Risk Detection – Retail Banking SensaAMLTM uses state-of-the-art AI and intuitive UI to organize large amounts of data based on similarity to reveal hidden relationships and groups of customers with deep meaning. These shapes help non-data science users easily interact with large data sets to identify patterns, anomalies, and hotspots like in our case study below using retail banking data. Group X has: • 3000 customers (1.37% entire population) • 40 times Level 3 Investigations compared to average Group X has: • • 193 customers • 156 times L3 investigation density • 42 never investigated • 37 high risk after review • 1-year early ide

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