The Alternative Data and AI Imperative for Inclusive Credit Decisioning

By Kim Minor , Senior Vice President, Global Marketing at Provenir

Gen Z, transitioning from school to the workforce, already has an estimated collective buying power that is nearing $150 billion. A TransUnion study found that one-half of Gen Z consumers in the United States alone are credit-active and have a credit card.

To serve Gen Z consumers, financial organizations must address the challenges and opportunities of onboarding Gen Z, including this generation’s expectations for digital finance, by reengineering their processes to be more inclusive of younger clients with low or no financial history. One study shows that just 47% of Gen Z respondents — versus 75% of Baby Boomers and 70% of Millennials — have an account with a traditional bank, credit union, neobank or technology company.

As a result, the traditional ways of accessing credit financial services often discount or exclude these consumers. With their minimal credit history, Gen Z can return low credit scores and may be denied the financial services they want and need.

Adopting alternative data and artificial intelligence to evaluate risk while promoting financial inclusivity

Financial institutions are embracing alternative data and artificial intelligence to improve credit decisions for Gen Z. This generation has only known life with a smartphone or the Internet. They offer more data about themselves than previous generations because they didn’t just grow up with technology; they are full-on digital natives.

This is a great opportunity to use alternative data for financial credit decisions for individuals with a thin (or no) credit file. With it, organizations can assemble a more holistic, comprehensive view of an individual’s risk. This includes income and employment information, social media, utilities/telecom payment history, rental payments, etc.

While alternative data is a great start to level-up credit decisions, financial services organizations need more automation, more sophisticated processes, more forward-looking predictions, and faster speed-to-decision. And to this end, they need AI and machine learning.

AI, machine learning and alternative data may have been on the credit risk decisioning “nice to have” list a few years ago, but fintech and financial services organizations quickly realised that legacy technology and approaches are simply not up to today’s task of credit risk decisions, especially when it comes to assessing the creditworthiness of Gen Z consumers as well as for other underserved consumers such as recent immigrants.

For unbanked and underbanked consumers, AI allows organisations to support those consumers’ financial journeys. Since AI can identify patterns in a wide variety of alternative, traditional, linear, and non-linear data, it can power highly accurate decisions, even for no-file or thin-file consumers. This vastly benefits those who can’t be easily scored via traditional methods while also benefitting financial institutions by expanding their total addressable market.

With AI, machine learning and alternative data, financial services organisations are on their way to improved agility and confidence in credit risk modelling. In doing so, they will be more prepared to cater to up-and-coming Gen Z consumers.