Data, Partner

How we can stop using the term ‘financial inclusion’

  • Our current credit system has led to decades' worth of errors and financial exclusion.
  • But as consumers get more autonomy in their financial decision-making, lenders may have to finally change their ways.

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How we can stop using the term ‘financial inclusion’

By Sarah Davies, Chief Data & Analytics Officer, Nova Credit 

This is the second of a three-part series addressing some of the major challenges in our current credit system – and how we can fix them.

When we think about financial inclusion, we often think of consumers that fall outside the realm of mainstream credit, who are considered higher-risk from a mainstream perspective and far from creditworthy.

The reality, though, is that these consumers simply don't have enough data on financial behaviors which the consumer credit risk industry deemed important in the '90s to determine creditworthiness. And because the industry automatically considers them higher-risk, these consumers aren’t eligible for fair access to financial products.

But the truth is that consumers produce financial data all the time – just not in the original and outdated qualifiers for creditworthiness and the ability to pay back debts. 

This is where the term ‘financial inclusion' has come in. 

Our industry has created this type of terminology to describe all these consumers that fall outside of the creditworthiness paradigm. 

But while the term helps describe the problem, it doesn’t solve it. The solution, rather, is something that’s happening right under our noses; with the advent of new data and new analytics techniques, we could potentially solve the problem of financial exclusion and no longer need to use the term ‘financial inclusion’.

But as an industry, we've yet to truly embrace these newer data sources, and are still holding on to old paradigms and systems. 

So who’s considered a ‘financial inclusion’ consumer?

Out of approximately 240 million consumers with information sitting in the credit files, about 180 to 190 million consumers are considered mainstream creditworthy, by having what we would call a ‘mainstream’ or ‘thick’ credit file. This means they have at least two trades and update their credit very frequently within a six-month window. 

The rest of the consumers have diminishing levels of information on their credit file.

Of those, 20 to 30 million have thin or sparse credit files, meaning they have at most two accounts, like a credit card in one hand and a student loan in the other.

After them, there are around 10 to 15 million infrequent credit consumers, who may have multiple accounts but don't update credit very frequently. 

Except for the ‘mainstream’ group of consumers, the rest are people who fail to get scored within mainstream credit scores like FICO, effectively qualifying as ‘financial inclusion’ consumers. As a result, around a quarter of US consumers are locked out of the credit system.

The problem with mainstream data and creditworthiness

The original DNA of the financial service system was established over 30 years ago. Since then, we’ve seen changes like the rise of BNPL due to market demand from younger generations who don't relate to the traditional credit system; financial inclusion initiatives iterating on the existing paradigm; or the creation of VantageScore, built on similar credit data as the FICO score.

While some would consider these adaptations, they are only addressing symptoms and not the core problem, which is that both the model and data of creditworthiness were established in the '90s, leaving much of the modern consumer out of the equation.

FICO credit scores are embedded within almost all institutions, and indeed are very effective. The problem is that this is a statistical algorithm that requires a certain amount of data on specific behaviors in order to calculate the score. In other words, if you don’t have enough of the right data, you don’t get a FICO score.

In a way, the system is quite similar to a Russian doll: once you open the various layers of the doll, and you get to the very central part, you see that it is highly predicated on this particular algorithm that requires just enough of the right data. 

Bottom line

We need to move out and move on. This means embracing more expansive credit risk models, new data sources like bank data and cash data, and new technologies. Who knows – maybe then we could actually be financially inclusive.

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