Top 3 Challenges for Automated Loan Underwriting

Automation and artificial intelligence have been key players in financial services for years. Financial institutions have automated routine tasks across fraud detection and prevention, loan application processing, and customer service, to name just a few. But the pace and breadth of adoption will only increase. 

In the lending market, automated underwriting can be a game-changer. Automation tools can help offload manual underwriting tasks, like pulling credit reports or tax returns, while still allowing humans to make final decisions. With higher interest rates increasing pressure on the lending industry, organizations must accelerate the lending cycle without increasing risk.

Banks, credit unions, and lending institutions can speed risk assessments by automatically performing verification checks, reviewing credit histories, or calculating risk scores. They can even automate portions of customer communications to provide status updates on loan decisions and increase customer satisfaction. 

But there are roadblocks that can prevent you from deploying AI, machine learning, and automation across the full enterprise. Some that can leave you open to compliance risks. Understanding these challenges helps you avoid them and still reap the benefits of automated lending. 

Today, we’ll cover three critical loan underwriting automation challenges—and how to overcome them to drastically improve the process.

The world runs on data. Automated underwriting is no different—it requires a bedrock of connected, accessible data to run successfully. But too often, financial institutions are plagued by disjointed and siloed data scattered across multiple systems. Worse, data can be outright incorrect. When loan officers don’t have accurate info when reviewing employment histories, credit scores, or tax returns, underwriting decisions suffer. 

Fortunately, there are several answers to this problem.

First, data fabric. A data fabric architecture connects disparate data systems together into a unified data model. By operating in this virtual data model, organizations gain a 360-degree view of their enterprise data. This makes data more accessible for use in automated processes (or for humans to review). Plus, data fabric syncs information across disparate systems so information is always kept up to date. 

Second, use APIs and RPA to make sure data remains complete. As mentioned above, a data fabric can help keep data sources up to date. But automated loan underwriting also requires connecting to external information sources like background check databases and credit bureaus before making a decision. While you can manually pull this information, doing so is time-consuming and error-prone. In this case, you can often connect to these databases via API. No API available? No problem. Robotic process automation (RPA) can help. In some AI process platforms, you can simply record a human taking action on a website such as pulling information or entering data into forms, then generate a bot from that process. This enables financial institutions to cut down loan processing times, leading to cost savings and improved experiences for loan applicants.

Automation is not all-or-nothing. One of the main drivers behind AI and AI worries is runaway models—from sci-fi-based fears of AI taking over the world to more realistic, present issues like insurance companies automatically denying claims en masse.

Automation can run amok if unsupervised. That’s why it’s critical to keep human underwriters in the loop. Yes, human error can still occur. But keeping humans in the loan underwriting process adds a check to help organizations avoid the biggest risks of automation.

And process orchestration tools are the real hero in this story. Using a process automation platform, you can coordinate work between humans and digital workers. For example, in a simple workflow diagram, you can direct your systems to collect data via API, make calculations, format information via AI, and then send information to an underwriter to review before making a decision. By orchestrating work using business rules, teams can dramatically reduce potential automation or AI errors.

Regulations are increasing, and AI has made compliance more complicated than ever. Artificial intelligence has become a particular focus for regulators. This is with good reason—the implications of AI on the workforce are still developing, but bias in decision-making has surfaced as a major stumbling block. AI bias (and automation bias in general) refers to entrenching human biases into automated systems. 

This must be attacked on two fronts. First, when training AI models, make sure to eliminate data elements that could lead to biases. For example, you’ll want to scrub any information that might be connected to a protected class. Any kind of PII marker that isn’t relevant to the decision should be eliminated to minimize risk.

Second, include humans in the loop during loan decisioning. While humans make mistakes as well, it’s important to keep them as a check against any automated decisions. Having humans double check information and make the final call helps reduce the potential risk of biases.

Automated loan underwriting isn’t just about saving money—although, you absolutely will. With automation, you can free up workers to do higher level tasks or place them on strategic projects that open your organization to new revenue sources. And you can offer a better customer experience by approving loans faster and offering greater transparency to customers. 

So when it comes down to it, the greatest risk of automated loan underwriting is to not pursue it. Because if you don’t, your competition will. By taking precautions to prevent risk exposure, you can safely leverage AI and automation to streamline the loan decisioning process and reap the full benefits of automation. 

Want to learn how? Download Unlock the Power of Connected Underwriting.