Organizations are under pressure to improve their financial crime compliance, while at the same time trying to reduce operational costs and improve customer experience. Balancing these demands requires a reconsideration of operating models for antimoney laundering (AML) and know your customer (KYC) compliance, and the re-engineering of supporting IT infrastructure and applications.
An intelligent, driven approach requires organizations to improve their use of data and advanced analytics. This can be supported by artificial intelligence and machine learning. The aim is to reduce false positives and detect suspicious activity early, while managing the cost of operations.
Volume, velocity, veracity and variety of data transactions is driving banks to redesign their data, process, application and technology architectures.
The amount of data required to meet financial crime compliance is increasing at an exponential pace. Current data and analytical infrastructure is unable to cope with this increase.
The accuracy of the data also needs to improve significantly. Data needs to be current at all times. Firms are moving away from periodic reviews of KYC data, and are instead using changes in material data to trigger reviews.
It is also important to dissolve organizational silos. This allows for the sharing of data, customer behavior and intelligence, to further increase the quality of the data and its ability to help detect money laundering and fraud. In the past banks set up operational and technological silos along business units or product processors, resulting in higher costs for financial crimes compliance. Existing technology infrastructure has limited capacity and is unable to support the riskbased approach recommended by regulators.