How Augmented Analytics Can Fight Financial Crime

Machines can serve an important role in mitigating and anticipating the threat of financial crime, but humans have their part to play as well.

People working in connection with analytics tools can ensure customers are protected from disruptions like false positives as algorithmic-driven transaction monitoring occurs.

This combination of human intelligence and machine learning makes the world a safer place in which to transact, but it also means people need to be able to initiate intelligent rules-setting when working with their analytics suite.

Emphasizing the quality of human experience to separate legitimate transactions from illegitimate ones is essential for businesses that want to improve customer experience without giving up security.

This is where augmented analytics — the deploying of technology to glean added insights, risk coverage and greater value from the investments and the control framework already in place — comes into play.

Augmented analytics is more than just rules-based transaction monitoring, as it offers data-driven insights that are based on its users and the data it collects. The right augmented analytics solution gives precise knowledge to help organizations prepare, create and explain their insights.

In addition, augmented analytics make it easier to scale anti-financial crime protections by giving more weight to the value of human intelligence in transaction monitoring and compliance reporting. Organizations can leverage AI to come up with robust predictive insights that let them assess, model and deal with the risk of money laundering and fraud at scale.

Finally, augmented analytics make transaction and consumer data easier for organizations to manage while ensuring entities’ access to data insights is as simple as their ability to review data on a per-transaction basis.

Deeper analytics create a more accurate transaction risk scoring and a more comprehensive view of customer behavior, transaction patterns and security risks. These allow organizations to create long-term anti-fraud and AML strategies that are based on historical data and real-time insights by following a broader range of legitimate consumer behaviors.

For more on how organizations are employing augmented anti-money laundering measures, download your copy of the Augmented AML And Risk Management report, a PYMNTS and Featurespace collaboration.