The financial services landscape has been radically evolving for the past decade, and credit scoring is no exception. Once the domain of a small number of credit bureaus, alternative credit scoring is democratising the credit underwriting process for banks and other lenders. This global trend shows no signs of slowing down, especially with the rise of digital-only banks and as more and more people use banking in unconventional ways, e.g. applying for a mortgage online.
Traditional credit scoring models don’t hold up for a significant proportion of consumers globally, especially among those with thin or no files like millennials, members of Gen Z, refugees and immigrants. In the European Union, for example, more than 37 million people still lack access to formal financial services, that is more than 7% of the population.
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This is not only a problem for vulnerable groups that have little or no access to banking services but is a tremendous missed opportunity for financial institutions, as they miss out on good customers that are uncovered by conventional methods.
Alternative credit scoring is not only the future but the present! It allows financing institutions to lend more responsibly and help more qualified customers. With ever-increasing mountains of data at our fingertips, alternative scoring promises more accuracy and expanding access to a more significant portion of the global economy.
Some methods are already in place to help bank the underserved and minimise risk. But why are alternative methods so necessary at this time?
The Benefits Alternative Data?
Beyond the more obvious benefits to financial institutions, expanding credit can serve to revolutionise smaller markets where there may be little to no access to banking. In countries with a long-standing banking infrastructure for those with little to no credit history, alternative credit scoring can open up a world of possibilities for economic growth; it can open doors to better education, better healthcare and a better life.
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Even those consumers with existing files and access to banking can enjoy the benefits. Factors like paying bills on time can boost scores, and improve affordability. Fully digitalised and faster approval processes, which are made possible using alternative data also mean a smoother, more streamlined user experience for customers.
Transactional data in credit scoring
How does alternative credit scoring work? Well, the opportunities being created by PSD2 make it possible for financial institutions to take advantage of new data sources, such as transactional data from bank accounts, which increases the possibilities of what you can do. There are several ways to generate alternative scores in the era of big data, data is silver but interpreting that data is gold.
Transactional data is particularly beneficial in credit scoring because not only can it verify fundamental information that helps with AML and KYC; assisting customers to prove they are whom they say they are and verifying an individuals income. But also because it can provide an up-to-date picture of a person and their financial behaviour.
Transactional data can be refined to illustrate behaviour trends in customers and identify changes in behaviour that cannot be easily detected by other data sources. For example, perhaps a person had a poor credit rating a few years ago but, in the last few months, they have shown more stable financial behaviours, which indicates this person should now be accepted for a loan.
Looking at transactions can accurately identify risky behaviours such as late night bank withdrawals. It can also be used to identify behaviours that are more frequently associated with customers that are most likely to make payments on time, such as paying utilities, mobile phone bills, and student loans on schedule is another measure that can determine the risk of lending to an individual.
The future is in transactional data
With developments in fields such as machine learning and the implementation of regulations like PSD2, it is easier than ever to use transactional data to create more customer-centric products and offers, which ultimately results in the ability to meet the demands not just of your existing customers but of your new customer base too.
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New banking API interfaces, made possible by PSD2, will improve the availability and structure of data. As cleaning data is one of the most resource-intensive procedures involved in machine learning transactional data that comes prepackaged and structured will improve the efficiency of the transition to machine learning. All of which is fundamental for automating credit underwriting flows.
With so much data available to financial institutions, it is only a matter of time before more and more lenders add transactional data to their credit scoring models. With the proliferation of FinTechs and online-only banks, it’s more important than ever for established players to find creditworthy individuals who are overlooked by traditional credit agencies.
Transactional data can boost accuracy, reducing risk and improve efficiency in decision making, thus ultimately improving the bottom line. Transactional data provides new methods to do business that meets the needs of consumers. As alternative credit scoring advances, its ability to accurately estimate a borrower’s risk can create a feedback loop where models get better over time, and credit is available for more and more people.
Learn more about how Instantor we can help you to use transactional data to access more consumers faster.