Case study

New Risk Detection – Retail Banking

04.26.2022 | By SymphonyAI team

Download

 

SensaAML™ 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.

Latest Insights

How SensaAI for Sanctions reduces false positives - AI for sanctions screening software
 
09.06.2024 Video

How SensaAI for Sanctions reduces false positives – AI for sanctions screening software

Financial Services Square Icon Svg
 
09.05.2024 Blog

5 reasons to move financial crime compliance to the cloud

Financial Services Square Icon Svg
Add AI and machine learning to rules-based transaction monitoring systems to enhance financial crime detection
 
09.03.2024 Blog

Add AI and machine learning to rules-based transaction monitoring systems to enhance financial crime detection

Financial Services Square Icon Svg