Consumer Lenders Still Struggle to Get With the Times

Data-driven best practices will help ensure a successful consumer lending transformation.

In today’s digital-centric environment, consumers expect faster everything – and turnaround time on a loan application is no exception. However, Cornerstone research and data indicate that most banks and credit unions are simply not in step with customer expectations. It’s 2023 and innovations like AI are gaining steam, but dated systems and clunky processes continue to clog the consumer loan pipeline in most financial institutions.

There are four critical stages in driving profitable, high-quality consumer loan growth:

• Awareness: making consumers aware of your offerings through marketing and digital engagement
• Application: getting the borrower to formally apply for the loan
• Approval: getting the borrower approved
• Close: taking the approval and getting the deal to closing and funding

Cornerstone looks at several metrics to track the effectiveness of this loan pipeline in closing deals and growing loan production. Today, many financial institutions are looking to drive a higher level of auto-decisioning using new underwriting engines built on new technology.

While we encourage lenders to build sophisticated auto-decisioning capabilities, implementing a system comes with a big price tag, and shiny new technology alone will not unclog the loan pipeline to create an optimal customer experience. Without an aggressive streamlining of processes and underwriting habits, a decision engine is just an overpriced calculator.

According to Cornerstone’s 2022 benchmarks, only 42% of financial institutions employ auto-decisioning in their consumer lending practices. Our firm would like to see that number much higher. Here are a few interesting stats that reveal lenders’ current evolution:

• Institutions that use auto-decisioning only approve one-third of their loans with this tool. While all loans cannot ever be auto-decisioned, Cornerstone has seen high performers operate at twice the decision percentage of current peer medians.
• Ironically, institutions that auto-decision their loans do not achieve a higher approval rate than those that still rely on underwriters (60% vs. 62%). The lesson here is that if the overall loan origination process is inefficient, a decision engine cannot overcome these shortfalls.

Cornerstone finds that the best lending performers get very gritty with defined key performance indicators that measure pipeline effectiveness and service level metrics like time-to-approval and time-to-close. Member satisfaction and net promoter analysis are also tracked closely by lending leaders.

Our research data indicates that lenders are not getting the full benefit of auto-decisioning engines because they haven’t completed an end-to-end streamlining of their processes. Here are some key insights that reveal the challenge:

• Auto-decisioning can create tremendous efficiency. Cornerstone benchmarks show that the number of consumer loan applications reviewed per underwriting FTE per month is 960 for institutions that use auto-decisioning, compared to 260 for institutions that don’t. This is a tremendous opportunity; however, many institutions take this efficient process and layer on human underwriter review, which slows the process and threatens the ability to get these loans closed.
• A decision engine alone is not the answer. Institutions that auto-decision have much lower approval and conversion rates than old-school lenders. This outcome is counterintuitive, but Cornerstone believes it’s because institutions view decision engines as a panacea and fail to eliminate bad processes after the system makes a recommendation. Bad processes include further underwriter reviews and collecting borrower information for close that was not captured in the original application.

While the business case to leverage new automated decision engines is strong, leaders need to ensure this investment is accompanied by a hard look at the people and processes that stand beside the technology. A comprehensive consumer lending transformation will include taking these actions:

• Review end-to-end processes and remove steps that clog the pipeline
• Ensure data integration from marketing and core systems to remove friction
• Review underwriting and approval criteria and algorithms to eliminate overkill
• Ensure the loan origination system is being fully leveraged in processing, closing and funding capabilities
• Train front-line and back-office staff to be knowledgeable and data-driven lending professionals
• Manage all stages of the loan pipeline with visible goals and KPIs

Transforming consumer lending will require that the bank’s or credit union’s leadership gets traditional sales, credit and operations professionals out of their comfort zone and into the fast, data-driven world of smarter consumer lending.