The Alternative Data and AI Imperative for Inclusive Credit Decisioning


It’s no surprise that financial institutions are fully focused on understanding the needs and preferences of younger consumers.

Already, Gen Z, which is transitioning from school to the workforce, 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 just 47 per cent of Gen Z respondents — versus 75 per cent of Baby Boomers and 70 per cent of Millennials — has 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 types of consumers. With their minimal credit history, Gen Z can return low credit scores and may be denied the financial services that they want and need.

Adopting Alternative Data and Artificial Intelligence to Evaluate Risk While Promoting Financial Inclusivity

To improve credit decisioning for Gen Z, financial institutions are embracing alternative data and artificial intelligence. This generation has never known life without a smartphone or the Internet. In fact, they offer more data about themselves than other generations before 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 decisioning 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 can include income and employment information, social media, utilities/telecom payment history and rental payments, and more.

While alternative data is a great start, to really level-up credit decisioning, financial services organizations also need more automation, more sophisticated processes, more forward-looking predictions and greater speed-to-decisioning. And to this end, they need Artificial Intelligence (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 fintechs and financial services organizations are quickly realising that legacy technology and approaches are simply not up to today’s task of credit risk decisioning, 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 gives organizations the opportunity 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 decisioning, 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 and machine learning and alternative data, financial services organizations 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.

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