The following is a guest post from Kim Minor, Senior Vice President, Global Marketing at Provenir.
The most significant transfer of wealth in U.S. history is underway as Baby Boomers begin transitioning assets to younger generations.
Over $70 trillion is in motion, underscoring the need for financial institutions to fully invest themselves in understanding the needs and preferences of younger consumers.
Gen Z, which is transitioning from school to the workforce, already has an estimated collective buying power that is nearing $150 billion. A study by TransUnion found that one-half of Gen Z consumers in the United States that are credit active have a credit card. The most popular credit products for Gen Z are credit cards (41 percent), student loans (37 percent), auto loans (23 percent), and private label cards (20 percent).
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 percent of Gen Z respondents—versus 75 percent of Baby Boomers and 70 percent 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.
Embracing alternative data and AI 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. 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 an excellent 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. Still, fintechs and financial services organizations quickly realize that legacy technology and approaches are not up to today’s credit risk decisioning, especially when assessing the creditworthiness of Gen Z consumers and other underserved consumers such as recent immigrants.
For unbanked and underbanked consumers, AI allows organizations 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 helping financial institutions expand their total addressable market.
With AI, machine learning, and alternative data, financial services organizations are on their way to improved agility and confidence in credit risk modeling. In doing so, they will be more prepared to cater to up-and-coming Gen Z consumers.
