Joe DeCosmo has 25+ years of experience in technology, machine learning and AI. He is Chief Analytics and Technology Officer at Enova.getty
Technological change accelerated during the pandemic, leading many people to adopt new ways to complete everyday tasks. Online tools and mobile applications have exploded for everything from shopping to food delivery and even financial services.
Fintechs have led the way in providing working people with online access to financial services regardless of where they live, what they look like or whether they have an imperfect credit history. Doing so requires technical innovation. For example, most banks and non-bank financial services companies are looking for a full range of automation technologies, from robotic process automation (RPA) and machine learning (ML) to artificial intelligence (AI) approaches that combine human judgment and AI intelligence.
A significant focus of lenders and industry stakeholders, from analysts to regulators, has been concentrated on underwriting—using automation to support or fully replace credit decision making. While this application has helped improve access to credit for millions of people, full implementation of ML and AI goes beyond that.
From Artificial To Augmented Intelligence
Lending companies can look at AI more broadly.
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RPA, ML and AI can come together as a holistic set of support mechanisms for internal staff and customers. The main difference between ML and AI is the difference between learning and decision making. I take an even broader approach, viewing automation as “augmented intelligence” that increases the efficiency and effectiveness of a team while giving customers a better experience. ML and AI can allow lenders to look beyond a credit score and see data patterns that can help establish the character and capability to repay that are the foundation of lending.
Companies can deploy AI throughout the customer life cycle. While credit approvals and product offers undoubtedly benefit from AI, it can also support everything from the application process to providing proactive customer support that helps borrowers repay on time and in full. For instance, ML can help predict answers to frequently asked customer questions, assisting both live agents and chatbots. Many in the insurance industry have already seen the benefits of AI related to self-service options. As a result, customer service agents can spend their time on higher value-added activities. Doing so is critical when working with people who have less-than-perfect credit. The extra time a service representative (or a smart chat tool) spends with an applicant working through their questions or information can help them access the services they need.
In other words, I see these technologies as a way to address unnecessary friction and empower teams to work better and smarter rather than a convenient way to reduce headcount.
A Broader Set Of Use Cases
Many banks have begun using AI to automate certain functions and reduce the processing time for loan applications. I also believe that using AI across the entire customer life cycle can further open up credit to assist underserved populations, including those whom banks often reject. Especially for subprime and near-prime borrowers who face many challenges accessing credit, cumbersome aspects of the process can discourage—and, therefore, exclude—potential borrowers.
Inexperienced borrowers who do not have a robust credit history provide a good case in point. Their credit scores do not necessarily reflect their likelihood of repaying a loan, but looking at their complete tradeline data can offer more detail about the timeliness of their payments and other behaviors that may qualify them for certain lending products.
Similarly, inexperienced borrowers may not have the same degree of documentation needed to verify their assets. While a human reviewer could make biased decisions based on documentation quality, banks, transaction sources, sources of cash deposits and more, AI could provide an opportunity to avoid such biases and make more equitable decisions on approval, loan products and interest rates.
The Full Lending Life Cycle Approach
When used correctly, augmented intelligence can achieve two aims: lower friction at every point in the lending life cycle and, as a result, lead to more successful outcomes for borrowers (i.e., full repayment within their loan terms). I have learned several important things in our AI journey at Enova.
Most notably, subprime customers are unique, and it is essential to tailor AI to their needs. AI can augment the capacity to identify risk profiles and assess the ability to pay outside of the typical target credit score. It can also make this possible at scale, enabling lenders to reach nearly one-third of consumers in the U.S. with subprime credit. With these consumers, a seamless process can make the difference between these individuals having their financial needs met or overdrawing a bank account. Over time, AI and the entire organization can evolve to meet the needs of this customer base.
In addition, AI can empower companies to tailor their support to increase success rates. Time-consuming, complicated customer support can discourage borrowers to the point of causing them to miss payments and then struggle to catch up. However, AI applied to customer support can anticipate their needs and predictively help them receive an answer quickly. As a result, the barrier to success is lowered. It can even be applied proactively to identify potential issues such as the success or failure of their next-debited payment.
When AI augments rather than replaces good lending practices, it can support fairer outcomes and improve financial well-being. The goal of any lender should ultimately be to help the customer be successful. AI can provide the means to achieve that end.
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