Do bots understand
risk?
A financial institution addresses what AI does when no one is looking
AI TEACHES ITSELF. BUT CAN IT LEARN RISK INTELLIGENCE?
The Situation
A financial services company had a problem. It faced increased risk exposure from its artificial intelligence (AI) due to inconsistent monitoring, risk identification, governance, and documentation of multiple applications across its business units.
It had to be addressed. The issues potentially exposed the company to poor customer experiences; negative brand image; and legal, regulatory, and compliance violations.
How was this happening? Their AI models and applications were generating results quickly, sometimes within a few hours. And by their nature, AI models have an inherent ability to learn and make algorithmic adjustments to optimize their performance.
The organization’s executives realized that they didn’t have a robust mechanism to manage the risks and ensure the AI algorithms operated within the guardrails of how the company intended them to operate. Further, information on vendor AI models was limited, constraining the ability to identify risks.
The company wanted help managing existing AI risks and to develop a rigorous process for keeping a watch on emerging ones. But to do that and perform risk assessments quickly, the company had to expand its data science, statistical, and risk management capabilities.
THE SOLVE
AI DIDN'T JUST
GET MORE
INTELLIGENT. IT
GOT MORE HUMAN.
THE IMPACT
Thanks to Trustworthy AI, our team:
- Brought understanding of how AI applications can generate outcomes devoid of business context when left unchecked
- Created a consistent classification and approach to AI algorithms and techniques
- Met or exceeded industry benchmarks in AI governance capabilities set by peer organizations
- Improved AI safeguards, transparency, and confidence across businesses with policies to determine who is responsible for the output of AI system decisions
- Put in place an agile and targeted operating model to manage AI adoption in a responsible manner with appropriate governance and controls
200+ AI models identified
Providing a better understanding of the definition, taxonomy, and application of AI across the company
60+ AI models assessed
Identifying the risks associated with AI use and deployment
Trust in AI adoption
Creating confidence via a defined operating model and rigorous structure to manage AI responsibly
Models for the future
Setting up systems to survey AI models and algorithms as they learn over time, not merely on day zero or day one