AI-driven lending platforms, such as fintechs and peer-to-peer networks, continue to make important strides in personalized banking experiences, all with a single tap. Their proliferation is largely because they’re rewriting the rules when it comes to competition, customer expectations, and efficiency. Because they are not attached to outdated processes and have emerged as entities outside of the traditional banking system, they can adopt user-friendly experiences and innovative technology.
This contrasts sharply with the approach that banks often use, such as taking days to approve loans or determine risk assessment. With customers now expecting immediacy, integrating AI tools is a good way for banks to remain competitive and stay ahead of the curve. By embracing AI tools, banks can significantly modernize their systems and improve customer satisfaction. Here’s more about what banks can learn from AI lending networks.
Speed & Automation
AI networks can process loan approvals in minutes, which is a dramatic improvement over the lengthy timelines associated with the traditional banking process. This speed is enhanced by automating crucial steps, such as data entry and analysis. AI can analyze large amounts of data much faster than the time it would take to perform the same job manually. AI can also determine creditworthiness by analyzing a broader set of points simultaneously. This same process can take weeks with the traditional banking process.
Traditional banks have not made this radical shift. However, banks can adopt automated underwriting to not only cut costs but also to improve customer experience. Some of the stages that can be automated include e-signatures, data entry, verification, and digital underwriting. By reducing the number of steps involved in applying for a loan and automating others, banks can significantly accelerate their loan approval process. With automation comes a reduced likelihood of human errors, too. In short, automation can fulfill two needs: analyzing large amounts of data without human error creeping in.
Smarter Risk Assessment
Besides the speed that AI lending networks bring to the table, they also use alternative data, such as utility bills and rent payments, to assess creditworthiness. By analyzing a wider range of data, AI lending platforms provide a more comprehensive and smarter risk assessment of a borrower. This type of assessment is particularly helpful to borrowers who don’t fit the traditional definition, such as freelancers or young professionals. Instead of being denied a loan, these applicants have a better chance at financial opportunities.
What can traditional banks do to work “smarter”? They can go beyond FICO scores to capture a fuller picture of borrower creditworthiness. A fuller picture often translates to a more accurate risk prediction as well. By doing so, they reduce the likelihood of rejecting a reliable candidate who doesn’t fit into their traditional definition. In addition, smarter risk assessment can help banks differentiate between low- and high-risk applicants. This more in-depth analysis ensures a more balanced, precise, and fair assessment.
Personalization at Scale
Fintechs can tailor their loan terms based on borrower profiles and a broader set of data, such as e-commerce transactions, cellphone bills, and banking transactions. This detailed analysis of a borrower’s profile not only ensures a more comprehensive analysis but also a more personal one. For example, a loan applicant who shows consistent bank deposits from a gig job may show financial reliability even if their traditional FICO score isn’t ideal. By analyzing different sets of criteria, fintech lenders can make decisions that better reflect a borrower’s financial reality.
Traditional banks often rely on a “one-size-fits-all” approach to lending and borrowing, often missing details like a stable employment history or consistent cash flow into a bank account. By personalizing the loan-lending process, banks can tailor their loans to the borrower’s specific cash flow. For example, repayment dates could be aligned with payroll dates, providing borrowers with a more realistic window to repay their loans.
Customer-Centric Experience
With more and more people using their mobile phones as personal computers, AI lending platforms have figured out how to tap into this by emphasizing mobile-first applications instead of in-person services. Mobile-first applications allow borrowers to manage their loans at any time and from anywhere. For instance, borrowers can submit their applications, upload the required documents, and track their approval status from their mobile phone. And if need be, they can even chat with support staff.
On the other hand, traditional banks often require borrowers to come in during specific bank hours and conduct in-person transactions. This can be time-consuming and cumbersome, especially for those who can’t take time off from work or who don’t live near their local branch. In addition, complicated paperwork and wait times contribute to their frustration.
To improve this process, banks should simplify their user interfaces and invest in digital-first journeys. This doesn’t just translate to using a mobile app. Banks should provide a seamless end-to-end experience, offering features such as push notifications to inform borrowers of loan approval status and AI-powered chats that can answer questions in real-time.
Banks Can Reclaim Their Place in the Lending World
To reclaim their place in the lending world, adopting AI tools is no longer optional; it’s a necessity. Banks can integrate every stage with AI-driven practices, whether that’s the underwriting process, data entry, analyzing risk assessment, or even assisting borrowers with frequently asked questions. In return, banks will get a faster decision-making process, a smarter risk assessment, a larger pool of diverse and reliable borrowers, and better customer satisfaction. More importantly, integrating AI-driven practices can create a more personalized experience for borrowers. By doing so, banks can ensure they remain competitive in a constantly evolving financial landscape.