how artificial intelligence can help you detect frauds and financial crimes, speeding up legitimate transactions and optimizing your business.
Systems that use machine learning essentially attempt to confirm the credibility of operations and identities by crossing information from several different sources. They analyze the data provided by each segment to create and identify patterns and to detect the profiles that fall outside the rule. As fraud is confirmed or not, the software relearns and adjusts itself automatically in case of failure. This kind of solution can perceive the relations “hidden” between data, that is, that would not be identified by people or systems fed only by definitions created by humans.
Another great differential of antifraud systems with machine learning compared to traditional models is the greater assertiveness. When we put the machine to learn with its own evaluations, the refinement is much greater than when we simply stipulate generic rules. In addition, these solutions can catch more quickly the new techniques that the fraudsters are developing.
These fraudulent schemes are being conducted by individuals and sophisticated criminal organizations, who are technology pioneers and are constantly evolving their tactics. Consequently, the organizations have to base their anti-fraud operations on scalable and dynamic technologies and processes with sufficient intelligence and agility to handle the large volume of occurrences and to continually adapt to new patterns and suspicious activities.