AI-based
solutions can shape how financial services businesses make money, whether the
business model is the same or not. For an existing financial services business,
the motivations may vary and range from efficiency to expanding the business. There
would be project risk as with any development, but leaving that important
consideration aside, it is worth bearing in mind that AI-based solutions would
also impact the risk profile of the business. This may or may not be the
original intention, but it becomes more likely. The key implication is that
implementing an AI-based solution would require a radically different risk
oversight approach by the business.
Standard
computer algorithms which are not AI-based can—and do—solve complex problems. The main feature of such
algorithms is that the problem is somehow defined and an algorithm developed to
solve it which will produce the same answer as long as the same inputs are
provided. So a credit-scoring mechanism calibrated to capture a certain type of
client gives you just that.
The
answers offered by an AI-based system may change over time. New data is used to
reassess the underlying relationships and recalibrate the relationship between
the target variable and the potential explanatory variables. This “learning”
can also happen in a standard programme when there is a process of
recalibration. The difference is that in the case of AI, learning would happen
on a real-time basis—that’s
the essence of AI.
Alternatively,
with AI a target variable may not have been defined. That’s not as unusual as
it might sound. For example, algorithms assessing a loan or credit card
underwriting may fall in this category because there is no single rule to
predict a borrower’s likelihood of repayment. New data can lead to a certain
recalibration or can be used to identify new relationships between certain
data. For example, over time an AI-based system might identify that outstanding
debt is a better predictor of the likelihood of borrower repayment than repayment
history and penalise someone with a relatively good track record of timely repayments.
The
first type of AI-based solution is called “supervised machine learning” and the
second one “un-supervised machine learning”. The key difference is the extent
of autonomy that goes with the learning.
Consider
the potential impact on conduct risk of AI-based tools. One of the expectations
from Treating Customers Fairly (TCF) with respect to product governance is that
they are designed to meet the needs of identified consumer groups and are
targeted accordingly. This requires a clear business strategy, including
identification of the target market through a combination of qualitative and quantitative
research and oversight of the business to ensure that it is aligned with
initial expectations of customers and business generated. Take the example of
automated investment services covered in a recent FCA review. These providers would rely on some type of
AI-based solution, whether supervised or unsupervised machine learning. The
possibility of capturing different customers or the advice generated being
different from what was envisaged cannot be ruled out. The challenge is how to
put in place a monitoring approach which ensures that outcomes and risks which arise
are consistent with the expectations in the business plan.
Something
similar can apply from the perspective of credit risk, impacting the quality of
the portfolio and performance. Suppose you have been targeting retail customers
with a specific risk rating for a credit card business. If you roll out an
AI-based solution to enhance the efficiency of product underwriting, you would
need to have in place mechanisms to ensure that the credit quality of the
portfolio is consistent with your expectations—or else change those expectations. Both options are
fine. You may want to keep your target credit rating constant and seek more
volume, or perhaps you see AI-based solutions as a more robust tool to support
decision making and, in a controlled manner, can relax your target rating.
Regardless of your choice, you would need to put in place a credit risk
monitoring approach that is suited to the new AI-based solutions, as well as
ensure that the business understands the portfolio implications of “learning”
that is at the core of an AI-based solution system.
The
salient point to take away is that the roll-out plan of AI-based tools may
focus on the launch. However, the greatest challenge may well be the need to
provide for the ongoing and timely monitoring of the AI-based tools and their
integration in business governance and risk management, which I will cover in
the next post.
