There are many aspects of such an operating model. Some are practical, such as ensuring that the tools integrate with other parts of the business. In this post, I am focusing on the delegation of decision making to the AI tool – the choices that exist in most cases and the implications for the control environment. These are summarised in the figure below.
At one extreme of the delegation of decision making, you have AI tools that operate independently of human intervention. An example is algorithmic trading or an automated trading system which trade without any human intervention to use the speed and data processing advantages that computers have over a human trader. Interestingly, this also represents one of the few prescriptive examples of PRA intervention where it requires that a human has the possibility of stopping the trading system.[1]
At the other end of
the spectrum, there are AI tools used by experts in a professional
environment. For example, actuaries
might use machine learning techniques to undertake experience analysis and
support reserving work.
Between these two
examples, you have AI tools that provide a forecast or recommendation for consideration
by an analyst. For example, the AI tool
could provide a credit rating that validates a rating derived using more
traditional methods.
Another middle of
the road alternative is ‘management by exception’. This means that the AI tools have a degree of
autonomy to operate within a ‘norm’, which is inferred from historical
data. Cases that are outside the norm
are then referred to an analyst for consideration to improve and verify the
predictions.
These are business choices
and in turn have implications for the development process of AI tools. You would expect controls around data and
model documentation in all cases. But broadly
speaking you would also expect a tighter control and a more intense validation
for AI tools that operate more independently of human intervention. This includes the depth of model’s
understanding, including:
- explainability – why did the model do that;
- transparency – how does the model work;
- the impact on customers – e.g., the difference between Netflix recommendations and credit card underwriting.
The choices of operating model also have important
implications for staff training. AI tools operated by staff that have not been
involved in its development must be trained to the appropriate level to ensure
that the AI tool operates effectively.
For example, where ‘management by exception’ is adopted, staff would
need the appropriate knowledge and skills to deal with the exceptions.
There are important choices for the operating model into
which AI tools are deployed. These
choices have risk management and control implications and these choices may
change over time. An AI tool might start
operating in an advisory capacity. As
trust in the AI tool increases then the delegated decision making can be
increased.
These implications and choices should be considered as part
of the model design.
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* This post is based on my contribution to a virtual panel discussion organised by ActuarTech on AI Governance & Risk Management.
