Reduce False Positives
False positives in monitoring systems are the root cause of high compliance costs, eating away at operational resources and increasing the risk of regulatory penalties - now on average 12% of total bank costs.
Reducing false positives is essential to streamline operations, protect customer relationships, and ensure a strong standing with regulators.
Banks and other financial institutions today are burdened by the high costs and inefficiencies associated with false positives from inefficient monitoring systems. This frustrating situation not only wastes valuable resources investigating legitimate transactions but also exposes institutions to significant regulatory risk as attention is diverted from genuine threats.
Data is the cure
When data teams are starved of the necessary data, they struggle to build precise predictive models. Consequently, these systems tend to generate an excessive number of false positives, casting a wide net to capture potential threats. Unfortunately, this broad approach results in numerous legitimate transactions being flagged, causing wasted time and resources on investigating legitimate activities.
The crux of the problem lies in the limited dataset available to each bank or financial institution. By only analyzing transactions within their purview, institutions miss out on the valuable insights gleaned from a broader, industry-wide dataset. This restricted perspective prevents monitoring systems from effectively distinguishing between genuine threats and false alarms.
Federated learning offers an innovative solution to the data scarcity issue faced by financial institutions. By securely pooling data from multiple institutions, federated learning creates a more comprehensive dataset that empowers the network participants to improve their monitoring systems and reduce false positives dramatically.
With federated learning, institutions can collaboratively train their predictive models on an industry-wide dataset, all while keeping their data on their own servers. By tapping into a broader range of data, participants can fine-tune their algorithms, enhance detection capabilities, and better identify patterns and anomalies indicative of fraud and money laundering. As a result, institutions can expect to see a significant decline in false positives, leading to increased efficiency, reduced operational costs, and more focused attention on genuine threats.
The FATF has examined several case studies on federated learning with outstanding results, showcasing the transformative potential of this technology in enhancing monitoring accuracy. Even closer to home, in Norway, limited data pooling experiments have led to an impressive 25-50% reduction in false positives. These findings serve as a strong foundation for our federated learning solution, offering a promising lower bound for expected performance improvement.