Financial services identity verification has traditionally relied on rigid, rules-based computer code. This hard-wired approach leaves no place for nuanced decision-making when determining who participates in financial transactions.
With artificial intelligence still in its relative infancy, risk engines were dependent on human interpretation of an analysis of known outcomes. This was a small step in the right direction, but still had a principal limitation: it required actual eyeballs. As machine learning has grown out of AI, new models have been generated to more effectively predict fraud. The more data a learning engine can train on, the more accurate its models and predictors. In an open-loop, continual learning mode, the engine keeps acquiring and learning from data, allowing it to keep digesting and learning.
This white paper outlines how machine learning and artificial intelligence can be used to combat the growing complexity of identity fraud based attacks at the highest level of scale and accuracy possible.