Smart Agents is the only technology that has the ability to overcome the limits of the legacy machine learning technologies allowing personalization, adaptability and self-learning.

Smart Agents technology is a personalization technology that creates a virtual representation of every entity and learns/builds a profile from the entity’s actions and activities.  In the payment industry, for example, a Smart Agent is associated with each individual cardholder, merchant, or terminal.  The Smart Agents associated to an entity (such as a card or merchant) learns in real-time from every transaction made and builds their specific and unique behaviors overtime.

There are as many Smart Agents as active entities in the system.  For example, if there are 20 million cards transacting, there will be 20 million smart agents instantiated to analyze and learn the behavior of each. Decision-making is specific to each cardholder and no longer relies on logic that is universally applied to all cardholders, regardless of their individual characteristics.  The Smart Agents are self-learning and adaptive since they continuously update their individual profiles from each activity and action performed by the entity.

Let’s use some examples to highlight how the Smart Agents technology differs from legacy machine learning technologies.

In an email filtering system, Smart Agents learn to prioritize, delete, forward, and email messages on behalf of a user. They work by analyzing the actions taken by the user and by learning from each.  Smart Agents constantly make internal predictions about the actions a user will take on an email.  If these predictions prove incorrect, the Smart Agents update their behavior accordingly.

In a financial portfolio management, a system consists of Smart Agents that cooperatively monitor and track stock quotes, financial news, and company earnings reports to continuously monitor and make suggestions to the portfolio manager.

Smart Agents do not rely on pre-programmed rules and do not try to anticipate every possible scenario. Instead, Smart Agents create profiles specific to each entity and behave according to their goals, observations, and the knowledge that they continuously acquire through their interactions with other Smart Agents.  Each Smart Agent pulls all relevant data across multiple channels, irrespective of the type or format and source of the data, to produce robust virtual profiles.  Each profile is automatically updated in real-time and the resulting intelligence is shared across the Smart Agents. This one-to-one behavioral profiling provides unprecedented, omni-channel visibility into the behavior of an entity.

Smart Agents can represent any entity and enable best-in-class performance with minimal operational and capital resource requirements. Smart Agents automatically validate the coherence of the data, perform the features learning, data enrichment as well as one-to-one profiles creation.  Since they focus on updating the profile based on the actions and activities of the entity, they store only the relevant information and intelligence rather than storing the raw incoming data they are analyzing, which achieves enormous compression in storage.

Legacy technologies in machine learning generally rely on databases. A database uses tables to store structured data.  Tables cannot store knowledge or behaviors.  Artificial intelligence and machine learning systems requires storing knowledge and behaviors. Smart Agents bring a powerful, distributed file system specifically designed to store knowledge and behaviors. This distributed architecture allows lightning speed response times (below 1 millisecond) on entry level servers as well as end-to-end encryption and traceability. The distributed architecture allows for unlimited scalability and resilience to disruption as it has no single point of failure.