The COVID-19 pandemic has brought several systemic financial issues into keen focus. Perhaps most notable is the worsening problem of financial exclusion, which continues to prohibit individuals and economies from achieving maximum growth. In light of the issue, major institutions, like the World Bank, as well as industry titans such as Deloitte, have now recognized an urgent need to implement new measures that address the challenge.
Recognizing the challenge
To begin with, let’s address exactly what we mean by financial inclusion. The term is admittedly broad, but normally refers to how easily individuals and businesses can access affordable financial products and services that meet their needs. Unfortunately, a significant number of people around the world currently find themselves on the wrong side of this challenge. In turn, the problem is affecting growth and heightening levels of poverty.
Financial exclusion poses a significant challenge for the banking sector too. Ultimately, financially excluded individuals and businesses are unserviceable for banks. As this problem worsens, the pool of potential customers for such businesses further dwindles. Therefore, as more people become excluded from accessing financial services, such as credit lending, banking businesses will also feel the pinch, which creates an incentive to change.
How do we fix it?
In truth, there is no single ‘fix’ to the issue of financial inclusion, but different sectors can enact new measures that partially address the challenge. For the banking sector, this endeavour will require an overhaul of traditional underwriting models, which increasingly look antiquated within a modern context. In fact, many of the screening and scoring processes used within the banking sector have remained largely unchanged in nearly four decades.
Unfortunately, the legacy measures implemented within the banking sector are adversely affecting levels of financial exclusion around the world. It’s now time for the industry to address the challenge head-on. This is not a case of ‘not fixing what isn’t broken’. The industry is being held back by its current reliance on these systems, and as mentioned, it’s beginning to damage their own profitability levels by excluding individuals from accessing financial services.
A moment for change
The need for change within the banking sector is beyond apparent. Therefore, the question for those in the sector is how it can be fixed. On paper, the sector needs to leapfrog 40 years of technological and data advancements. To do so, it’s essential for the current ‘one size fits all’ models of credit assessment to be abandoned, with more nuanced, tailored solutions coming to the fore instead.
Why is this so important? Well, for one, existing credit assessment models are ill-suited to servicing small-to-medium sized businesses (SMBs). Additionally, these models tend to discriminate against minorities, women, immigrants, displaced people, and those with limited family capital. Likewise, such models are unable to identify the fundamentals needed for business success, such as work ethic, tenacity, and community connectedness.
A new era for banking
Thankfully, there are new solutions, which can factor these qualities in, creating more responsive credit assessment models that better serve SMBs, as well as underbanked, underserved and immigrant individuals. Rather than being optimized for the established, included, and conventional borrower, these new systems enable banking companies to build models that better identify more relevant tenets of business success.
With such systems in place, the banking sector can begin to address the challenge of financial inclusion before it worsens further. Offering a considerable upgrade on what’s come before, these solutions can leverage billions of data points to identify and merit success factors as they are most clearly expressed across different market sectors. It’s a long overdue change, but now needs buy-in from major banking companies.
Can alternative data help?
In the past, lenders only had limited data to work off when assessing whether to provide SMBs with finance. This data, which was mostly derived from a business’ accounting and banking records, or from credit scores only provided limited insight into the true financial performance of a company. In fact, the information would often provide a misleading picture and limit the ability for SMBs to get finance.
Fortunately, alternative data is now helping to modernize this antiquated process by sourcing additional financial information from multiple extra sources. With this information, lenders can build a more accurate depiction of a business’ overall position. In general, alternative data can be categorized into three broad groups:
- External Market Attributes & Economic Indicators – Data taken from e.g., stock, currency, and real estate markets, GDP, the Consumer Price Index, and supply chains.
- Demographic Data – Derived from e.g., population numbers, migration patterns, employment rates, lifestyle shifts, and education levels.
- Exogenous Shocks – Data taken from e.g., COVID waves, interest rate changes, new regulations, political conflicts, and trade wars.
Additionally, raw data now can be extracted from several sources to further bolster alternative data. These sources can include credit card and POS transactions, website and mobile device data, Internet of Things (IoT) sensors, product reviews, social media, utilities, traffic patterns, and even satellite imagery.
Using alternative data to help the underbanked & underserved
In order to set up a small business, individuals must first have a bank account and an ability to provide evidence of creditworthiness. As a result, groups of underbanked and underserved people are immediately excluded from the process. This problem is systematic and particularly affects migrant communities.
If change isn’t forthcoming, many ‘credit invisible’ individuals will continue to be unable to contribute to the economy in a meaningful manner, despite having the necessary qualities and skills to build and operate a successful small business.
However, with access to alternative data, lenders can begin to make this much-needed change and start bridging the gap between the underbanked and underserved and the rest of the broader economy. Thankfully, there’s already evidence that its use is becoming normalised by credit lenders.
According to a survey by TransUnion, 34% of US lenders are now using alternative data to evaluate both prime and nonprime borrowers. More promising still is the 66% of lenders who report that they’ve been able to lend to additional borrowers, as well as the nearly two thirds of lenders who say they have already seen tangible benefits of using alternative data within the first year of adopting it.
Further progress will require the increased efforts of established lenders and credit bureaus, and the innovations of FinTechs. Should these efforts and innovations materialise, the outcome will likely be more favourable for everyone. In short, more equal access to credit for the unbanked and underserved will result in macro benefits for the global economy.
Building a more inclusive future
Therefore, should this change continue to be enacted, the banking sector can begin to do its bit to address the challenge of financial inclusion worldwide. What’s more, this adaptation will help financial services companies to serve a greater number of individuals and businesses, which in turn, should lead to an upturn in revenue and profit levels. Ultimately, it’s a win-win for all involved, and more necessary now than ever before.
Uplinq Financial Technologies is leading the fight to place new, modern credit assessment methods into the hands of small business lenders. With solutions like ours, credit lenders can leverage billions of alternative data points to generate scientifically validated lending decisions. In doing so, such systems are helping to better serve those currently excluded from traditional financial services and thus improving levels of financial inclusion.
This article was published by International Banker, and you can it here.