And AI is now expanding. For instance, many banks, insurance companies and other financial
institutions are implementing AI solutions as part of their risk management processes. Using
AI algorithms, these companies can now analyze and determine patterns of risk (based on
past incidents) to help identify and manage potential threats, scrutinize security issues, and
evaluate fraudulent activity.
The biggest challenge with AI resides with adoption. According to the research/advisory firm
Gartner, only half of AI projects make it from pilot into production; those that do make it take
an average of nine months to do so.
But Gartner sees that changing. "Innovations such as AI orchestration and automation
platforms (AIOAPs) and model operationalization (ModelOps) are enabling reusability,
scalability and governance, accelerating AI adoption and growth."
Machine Learning (ML)
Considered a subset of artificial intelligence, machine learning can be a particularly powerful
tool for prediction purposes. A key element in the burgeoning field of data science, ML uses
statistical models and algorithms to sift through tons and tons of data to identify
relationships or patterns that humans may not "see" or inadvertently ignore. The goal is to
uncover key insights to help drive better decision making throughout the organization.
One of the key benefits of machine learning is in its ability to run a multitude of variables
within the data to produce powerful predictive models. It's heavy computing power enables it
to do this thousands of times -- in split second timeframes. This enables it to "learn" from the
data and enhance its predictive capabilities.
Robotic Process Automation (RPA)
This simple but powerful technology helps to perform the more mundane -- but necessary --
tasks within an organization. These software applications or "bots" are able to execute
repeatable, logic-based activities to help efficiently scale business operations -- while freeing
up more experienced staff for more complex problems.
Steve Culp, Managing Director of Accenture Digital Risk and Compliance, sees tremendous
opportunities for RPA in the financial realm.
"In financial risk management, robotics can help identify and explain changes in risk exposure
and determine data-related or business-related causes for such movement. Robotics can
also be used to evaluate credit limits and determine causes for breaches in such limits, with
recommendations for remedial action generated automatically."
THE NEW RISK MANAGEMENT | 10
