Fraud prevention and anti-money laundering are tough use cases to tackle. They become even more difficult when data originates from various sources, not complete or correct, or even if it’s not kept up-to-date.
To make this work, machine learning models need the data in the right format, but data quality processes at Hadoop cluster scale are no picnic – and the moment that models gets put into practice, the data will be out of date. So, you’ll need a way to keep the cluster in sync with transactional source systems in real-time – which shouldn’t be too hard, right?
Register now for this July 19th webinar to explore solutions for using Hadoop, data quality and change data capture to deliver AML solutions at scale
Over the last few month’s our local (South African) DAMA chapter has been running a number of sessions discussing data modelling. Even in these days of “unstructured data” it seems that data modelling is needed more than ever. Why do[…]Read more...