Financial institutions must rethink how they build and govern valuation models as data becomes scarcer, markets more volatile, and AI more capable. A senior treasury executive argued that artificial intelligence should strengthen human judgment rather tha
Artificial intelligence may offer
financial institutions powerful new ways to improve valuation models, but it
will never remove the need for human judgment, governance and accountability,
according to a senior treasury executive speaking at a recent financial
services conference.
As banks contend with increasingly
volatile markets, illiquid assets and imperfect data, the speaker argued that
organizations should focus less on finding perfect models and more on
understanding the limitations of the information that feeds them.
"Garbage in is garbage
out," he said, describing poor quality data as the fundamental challenge
behind model performance. "Data is not good. And that is going to affect
the quality of your model."
Rather than viewing model risk solely
through the lens of mathematical techniques, the presentation emphasized that
valuation challenges originate much earlier in the modeling process.
Illiquid markets, sparse observations
and inconsistent data definitions all contribute to uncertainty long before a
model begins generating prices.
The senior treasury executive
illustrated the issue using the valuation of illiquid bank loans. Unlike
exchange-traded instruments, these assets often have little or no observable
market data, making traditional valuation approaches difficult to justify.
During periods of market disruption,
even products that normally trade actively can quickly become illiquid,
removing the very observations on which many valuation frameworks depend.
"If you don't have data,"
he said, "it's very difficult to come up with the right equation, let
alone calibrate your equation and come up with some parameters."
The consequences extend well beyond
technical model performance. Incorrect valuations can affect profit and loss,
trigger disputes with front-office teams and create difficult conversations
with auditors and supervisors who expect firms to demonstrate that their
pricing remains appropriate despite limited market evidence.
Throughout the session, the speaker
stressed that valuation is not confined to trading activities.
Fair value calculations influence
multiple areas of the balance sheet, from accounting disclosures and banking
book positions to credit spread risk and model risk adjustments.
For many institutions, the
overwhelming majority of assets remain effectively illiquid, creating ongoing
pressure to develop robust valuation methodologies despite limited data
availability.
The presentation highlighted several
practical challenges. Available market observations are often concentrated in
only a small number of products or borrower types, leaving large portions of
portfolios represented by only a handful of comparable transactions.
As additional risk characteristics
such as maturity, collateral, geography or sector are incorporated, available
observations become even thinner.
"Your data here is skewed,"
the executive explained. "You've got a number of buckets with a lot of
observations, but in most buckets you have not so many observations."
