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HOW DATA SCIENTISTS CAN CREATE A MORE INCLUSIVE FINANCIAL SERVICES LANDSCAPE

Adam Lieberman, Finastra
April 10, 2022 11:10 AM
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Image Credit: DKosig/Getty

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20 - 28. Join AI and data leaders for insightful talks and exciting networking
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--------------------------------------------------------------------------------



For those who understand its real-world applications and its potential,
artificial intelligence is among the most valuable tools we have today. From
disease detection and drug discovery to climate change models, AI is continually
offering the insights and solutions that are helping us address the most
pressing challenges of our time. 

In financial services, one of the main problems we are faced with is inequality
when it comes to financial inclusion. Though this inequality is driven by many
factors, the common denominator in each case is likely to be data (or lack
thereof). Data is the lifeblood of most organizations, but especially so for
organizations seeking to implement advanced automation through AI and machine
learning. It, therefore, falls to financial services organizations and the data
science community, to understand how models can be used to create a more
inclusive financial services landscape.

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Achieving a data mesh with Starburst and TSYS
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TOP ARTICLES







Achieving a data mesh with Starburst and TSYS



LENDING A HAND

Lending is an essential financial service today. It drives revenue for banks and
loans providers, but also provides a core service for both individuals and
businesses. Loans can offer a lifeline during difficult times, or be the boost
needed for a fledgling start-up. But in each case, loan risk must be evaluated. 

The majority of loan default risk today is calculated via automated tools.
Increasingly, this automation is provided by algorithms that greatly expedite
the decision-making process. The data that informs these models is extensive,
but as with any decision-making algorithm, there is a tendency to deliver
accurate outcomes for a majority group, leaving certain individuals and minority
groups disadvantaged, depending on the model used. 

This business model is, of course, unsustainable, which is why loan providers
must consider the more nuanced factors behind making “the right decision”. With
the demand for loans booming, particularly as point-of-sale loans such as
buy-now-pay-later offer new and flexible ways to gain credit, there is now a
wealth of competition in the industry, with traditional lenders, challengers and
fintechs all vying for market share. As regulatory and social pressure continues
to grow around fairness and equitable outcomes, organizations that prioritize
and codify these principles within their business and data science models will
become increasingly attractive to customers. 


BUILDING FOR FAIRNESS

When a loan risk model rejects applications, it’s possible that many of the
unsuccessful applicants will implicitly understand the logic behind the
decision. They may have applied knowing that they would not likely meet the
acceptance criteria, or simply miscalculated their eligibility. But what happens
when a member of a minority group or individual is rejected, based on the fact
that they fall outside the majority group on which a model was trained?

Customers do not have to be data scientists to understand when unfairness —
algorithmic or otherwise — has occurred. If a small business owner has the means
to pay back their loan, but is rejected for no discernible reason, they will
quite rightly be upset at their mistreatment and may seek a competitor to
provide the services they require. Furthermore, if customers from a similar
background are also rejected unfairly, then there is potentially something wrong
with the model. The most common explanation here is that bias has crept into the
model in some way. 

Recent history has shown insurance companies using machine learning for
insurance premiums that discriminated against the elderly, online pricing
discrimination and even product personalization steering minorities into higher
rates. The cost of these glaring mistakes has been severe reputational damage,
with customer trust irretrievably lost.

This is where there must now be a refocusing of priorities within the data
science and financial services communities, which elevates equitable outcomes
for all above high-performing models that work for the majority. We must seek to
prioritize people in addition to model performance. 


ELIMINATING BIAS IN MODELS

Despite regulations that rightly prevent the use of sensitive information for
use in decision-making algorithms, unfairness can creep in through the use of
biased data. To illustrate how this is possible, here are five examples of how
data bias can occur: 

 * Missing data — This is where a data set is used that may be missing certain
   fields for particular groups in the population. 
 * Sample bias — The sample datasets chosen to train models do not accurately
   represent the population users wanted to model, meaning the models will be
   largely blind to certain minority groups and individuals.
 * Exclusion bias — This is when data is deleted or not included because it is
   deemed unimportant. This is why robust data validation and diverse data
   science teams are essential.
 * Measurement bias — This occurs when the data collected for training does not
   accurately represent the target population, or when faulty measurements
   result in data distortion. 
 * Label bias — A common pitfall at the data labeling stage of a project, label
   bias occurs when similar types of data are labeled inconsistently. Again,
   this is more a validation issue.  

While no point in this list could be described as malicious bias, it’s easy to
see how bias can find its way into models if a robust framework that builds in
fairness is not included from the start of a data science project. 

Data scientists and machine learning engineers are used to very specific
pipelines that have traditionally favored high-performance. Data is at the heart
of modeling, so we start each data science project by exploring our data sets
and identifying relationships. We go through exploratory data analysis so that
we can understand and explore our data. Then it’s time to enter the
preprocessing stage where we wrangle and clean our data before we begin the
intense process of feature generation, which helps us to create more useful
descriptions of the data. We then experiment with different models, tune
parameters and hyperparameters, validate our models and repeat this cycle until
we’ve met our desired performance metrics. Once this is done, we can productize
and deploy our solutions, which we will then maintain in production
environments.

It’s a lot of work, but there is a significant problem that is not addressed
under this traditional model. At no point in this cadence of activity is model
fairness assessed, nor is data bias heavily explored. We need to work with
domain experts, including legal and governance, to understand what fairness
means for the problem at hand and seek to mitigate bias from the root of our
modeling, i.e., the data. 

Simply understanding how bias can find its way into models is a good start when
it comes to bringing about a more inclusive financial services environment. By
checking ourselves against the above points and reassessing how we approach data
science projects, we can seek to create models that work for everybody. 

Adam Lieberman is the head of artificial intelligence and machine learning at
Finastra 


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