The anti-financial crime landscape is continuously evolving, and financial institutions need to stay a step ahead of emerging fraud trends and regulatory compliance challenges to protect their customers and themselves from loss and reputational damage.
As consumers become increasingly reliant on the speed and convenience of digital banking products, institutions should consider end-to-end financial crime management solutions that offer real-time fraud detection, targeted AML transaction monitoring and automated regulatory reporting to fight financial crime and strengthen compliance. With artificial intelligence, including machine learning and robotic process automation (RPA), behavior-based anti-financial crime management solutions can help institutions increase the effectiveness and efficiency of their fraud and AML programs.
But artificial intelligence relies on the power of big data. Anti-financial crime management solutions need an immense data set from multiple sources, including core, ancillary, open-source, third-party and consortium data. Artificial intelligence can be applied to this data with cross-institutional analysis in a cloud-based environment. Solutions built with big data and artificial intelligence reduce false positives and increase the quality and accuracy of alerts.
Analytical agents built with machine learning algorithms continuously analyze data to improve analytical performance. A large, cross-institutional data set in a cloud-based environment allows machine learning agents to train on labeled data from thousands of institutions, achieving performance levels that cannot be matched by a single institution with a limited, isolated and restricted data set. Machine learning can significantly improve analytical performance, helping institutions reduce false positives and reduce the alert review time to increase the efficiency of investigations, while continuing to detect new and emerging criminal trends.
Machine learning agents use mathematical and statistical models to learn from data without being explicitly programmed. These agents analyze new data, including transactions, demographics and customer behavior, and utilize this evidence in transaction monitoring alerts. The alerts provide feedback to the cloud-based data set, where the training data, which includes the transaction, demographic and customer-behavior evidence, and labeled data, such as cases, marked transactions and return items, are input to a machine learning algorithm.
This data is used to train many different types of machine learning agents to determine which type of agent performs best for a particular typology. Before training an agent, the data is split into training, testing and validation data sets, so that the results of the training can be validated in an unbiased manner. Anti-financial crime management solutions can use precision, recall and false positive rate to validate analytical performance and assign the most suitable analytical agent to a particular fraud or money laundering typology.
Strengthening Processes with Robotic Process Automation
Institutions can leverage technology such as RPA to improve internal processes, strengthen anti-financial crime management programs and ensure regulatory compliance. Using RPA to improve workflow automation can save financial crime investigators time by reducing manual tasks, automating steps in alert triage and regulatory reporting processes, and through prepopulating and submitting reports with consistency, speed and accuracy.
Enhanced data collection through RPA reduces human error that occurs during manual data collection and transference. It also automatically and reliably integrates multiple data sources in your anti-financial crime management and compliance solutions.
Anti-financial crime management programs can use RPA agents to validate information populated in a currency transaction report or a suspicious activity report, automatically submit reports, intelligently package related alerts together, and automatically assign work to a team or an investigator. Such solutions can also automatically triage alerts and segment customers into appropriate risk categories, increasing the efficiency and effectiveness of financial crime investigations.
To stay ahead of financial crime trends, financial institutions should consider the benefits of cloud-based solutions that leverage artificial intelligence to increase the effectiveness and efficiency of anti-financial crime management and compliance programs.