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How Data Scientists Can Create a More Inclusive Financial Services Landscape

How Data Scientists Can Create a More Inclusive Financial Services Landscape
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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 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.

Data Is Crucial to Businesses

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. Data is the lifeblood of most organizations, but especially so for organizations seeking to implement advanced automation through AI and machine learning.

Hence, financial services organizations and the data science community must understand how models can be used to create a more inclusive financial services landscape.

Lending Drives Revenues

Lending is an essential financial service today. It drives revenue for banks and loan providers, but also provides a core service for both individuals and businesses.

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.

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.

Build a Culture of 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.

When a minority group member or individual is rejected due to not fitting the majority group on which a model was trained, they often face denial without recourse.

If a small business owner, capable of repaying their loan, is inexplicably denied, they’ll understandably feel mistreated. This could lead them to seek out a competitor for the services they need.

The data science and financial services communities should elevate equitable outcomes for all. We must seek to prioritize people in addition to model performance.

Eliminate Bias

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.

Here are five examples of how data bias can occur:

  • Missing data
  • Sample bias
  • Exclusion bias
  • Measurement bias
  • Label bias

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.

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