Credit Management November 2019

OPINION
Object of desire
Navigating customers towards a desired credit
score is fraught with challenges.
AUTHOR – Stephen Miller
WITH the advent of
GDPR and upon
advice from UK regulators,
there’s an
increasing expectation
on financial institutions
using consumer data for credit
risk modelling to explain how automated
systems make decisions. However, explanations
of credit risk models and decisions
don’t necessarily translate into an
ordered sequence of actions a consumer
can take to improve their score. The increasing
use of machine learning models
also makes it more difficult to generate
model explanations that can be translated
into actionable consumer behaviour. This
creates new challenges when it comes to
explicitly navigating customers to a desired
credit score.
Credit risk scores can be a confusing
and cloudy subject for consumers – they’re
often shocked to learn that ‘a’ credit score
was used in a decision instead of ‘the’
credit score. Multiple generic credit risk
scores exist, and these can be both useful
and beneficial for consumers to learn
more about the profiles of people that
are assessed as being sound credit risks.
However, lending decisions are often
based on a product-specific application
scorecard, and understanding the specific
score is more useful to a consumer looking
to be approved for a particular product,
such as a mortgage.
Suppose a consumer wants to reach
a given credit score or an approval
threshold. There are products in the
market that provide generic advice to
consumers looking to improve their
credit score, such as keeping credit
utilisation below 30 percent or not taking
out a mortgage more than three times
your income. But this advice may not
be relevant to a particular application
scorecard, or to an individual consumer’s
profile, and following the advice may be
infeasible for some consumers, such as
those with little to no credit history or
trying to repair their credit file.
SCORE SIMULATION
Other products simulate a consumer’s
own credit score using ‘what if’ scenarios,
intended to provide the consumer with
the knowledge of how their score would
change as a result of certain actions,
such as applying for a new credit card.
These solutions are more relevant to the
individual, but they do not generally
provide a path to reach a user-specified
score, nor do they provide a sequence of
actions that are necessarily achievable in
a fixed period of time.
In the USA, regulation requires that
the key factors that impact a credit score
must be provided alongside the credit
score. These factors are the items on
the credit report that have the largest
negative impact on the score, but in many
instances are not factors the consumer can
The paths are built
from reasonable and
appropriate monthly
steps, based on what
individuals with
similar profiles have
been able to achieve.
change in the short-term. For example, a
default or heavy search activity cannot
be instantly erased. Therefore, there is
a potential gap between what is causing
the largest negative impact to a credit
score versus what the consumer can do to
improve their score.
What is needed is an approach that
provides the consumer with a sequence of
feasible actions, achievable over a period
of time, tailored to their specific credit
profile, that will enable the consumer to
reach a desired score threshold.
The biggest challenge in developing
this algorithm is to properly define what
the feasible actions are for a particular
consumer, given their circumstances, to
overcome the pitfalls of trial and error
credit score simulators or generic advice.
Secondly, the algorithm must generate a
sequence of such actions that provide an
optimal path across a potentially complex
scoring surface, such as one generated by
machine learning algorithms, capturing
non-linearities and interactions.
Overcoming these challenges was
central to the work of our Data Science
Lab to generate optimal, feasible paths
that consumers can follow to improve
their credit scores. The paths are built
from reasonable and appropriate monthly
steps, based on what individuals with
similar profiles have been able to achieve.
When followed closely by the consumer,
the paths produce the desired score
increase. The advantage of this approach
over credit score simulators that rely on
trial and error and ‘what if’ scenarios is
that the steps proposed are more likely
to be achievable for the consumer, in
addition to being effective. The algorithm
can be applied to any score, requiring
only access to a representative sample of
anonymised consumer credit profiles and
scores over a short period of time. Access
to the scoring function itself is beneficial,
but not essential.
The optimal paths algorithm provides
feasible, actionable, and impactful recommendations
to the consumer, ensuring
they have the best possible opportunity to
make the changes they need to improve
their score.
Stephen Miller is Data and Analytics
Innovation Leader, Europe, Equifax.
The Recognised Standard / www.cicm.com / November 2019 / PAGE 33