Financial criminals find new ways for illicit activities with the development of technologies. However, the anti-money-laundering landscape evolves accordingly to fight them. Technology solutions designed to facilitate financial crime detection become more and more sophisticated. Advancements in machine learning have enabled firms to implement new technologies and processes that help anti-money-laundering (AML) investigators and compliance officers worldwide investigate criminal activities in the financial ecosystem.
Recently, regulators and law enforcement agencies discussed at FinTech & Regulation Conference how they could use new machine learning-based solutions to fight against global financial crime.
Financial crime is a complex area, and it isn’t confined by geographical borders. That makes it even more important for stakeholders to collaborate across country borders and organizations. Constructive dialogues and shared feedback between the public sector and the private sector are equally as important to learn about opportunities in the fight against financial crime.
Experts think that a key part in fighting financial crime is to improve information sharing. Ultimately, the private and public sectors will have to work alongside each other to create better conditions to fight financial crime.
Companies began data sharing process to fight the criminal activities
Most AML professionals encourage collaboration and information sharing between law enforcement and financial institutions. It’s important to guide institutions and people to become more comfortable and proficient in using new technologies. In recent years, people made many advances in the data sharing space. For example, federated learning is one of the types of machine learning technology that has emerged recently.
However, Federated learning is more about sharing data models and insights on what we learn from a breadth of data, and it doesn’t require sharing personal data. This technology will enable the companies to build best practices across the field. It allows them to build a common machine learning model without actually sharing the data across other organizations.
There are other ways to share information as well, such as homomorphic encryption. The latter allows computation on encrypted data. It lets data remain anonymous while it is processed and analyzed. With this method, organizations that share their data can retain control over which individuals get to see shared information.
Analysts also stress the effective impact of a machine and human collaboration. With technology becoming more sophisticated, many people worry that technology can make the human compliance officer obsolete. However, technology doesn’t aim to replace the human. It is there to enhance the work of compliance, as well as give compliance teams more time to spend on investigating crimes instead of manually sifting through massive amounts of data.