Artificial Intelligence (AI)
AI is the science of mimicking human thinking abilities to perform tasks that typically require human intelligence, such as recognizing patterns, making predictions recommendations, or decisions.
AI uses advanced computational techniques to obtain insights from different types, sources, and quality (structured and unstructured) of data intelligence to “autonomously” solve problems and execute tasks.
There are several types of AI, which operate with (and achieve) different levels of autonomy, but in general, AI systems combine intentionality, intelligence, and adaptability.
Machine Learning is a type (subset) of AI that “trains” computer systems to learn from data, identify patterns and make decisions with minimal human intervention.
Machine learning involves designing a sequence of actions to solve a problem automatically through experience and evolving pattern recognition algorithms with limited or no human intervention — i.e., it is a method of data analysis that automates analytical model building.
Respondents cite machine learning and natural language processing as the AI-powered capabilities offering great benefit to AML/CFT for regulated entities and supervisors.
Machine learning reportedly offers the greatest advantage through its ability to learn from existing systems, reducing the need for manual input into monitoring, reducing false positives and identifying complex cases, as well as facilitating risk management.
Machine learning applications are useful for detecting anomalies and outliers identifying and eliminating duplicate information to improve data quality and analysis.
For example, Deep Learning (DL) is an advanced type of machine learning in which artificial neural networks (algorithms inspired by the human brain) with numerous (deep) layers learn from large amounts of data in highly autonomous ways.
DL algorithms perform a task repeatedly, each time tweaking it a little to improve the outcome, enabling machines to solve complex problems without human intervention.