OPTIMIZE YOUR UNDERWRITING PROCESSES WITH AUTO LENDING ANALYTICS

Only a fraction of banks, credit unions, and fincos use the full power of auto lending analytics to their advantage. Legacy lending systems present a temporary roadblock to realizing the benefits of analytics. However, the advantages gained by moving to a modern loan origination solution with integrated analytics can have an immediate and lasting impact on a lender’s overall profit and portfolio growth. Auto lending analytics can be applied to all phases of the lending cycle. In this blog, we focus on the underwriting process and explain two ways analytics can be applied to optimize it: 1) assess process efficiency, 2) eliminate sources of risk. In the current auto market lenders need to employ the latest fintech capabilities to continually improve the efficiency of their lending practice.

Auto Lending Analytics Assess Underwriting Process Efficiency

Rapid response to auto loan applications is a major factor in booking loans. With decision rules and auto-structuring, lenders quickly respond to applications that match their credit policies. However, for most lenders, auto-approvals represent only a fraction of the decisions. The majority of loan applications are reviewed by underwriters. Workflows and automation facilitate the loan origination process, but underwriter experience is the key factor affecting individual productivity. Without analytics, it’s difficult to determine the efficiency—collectively and individually—of your underwriting process. A modern loan origination system tracks the details of application arrivals, applicant credentials, and time spent in every step of the underwriting process. By applying analytics to these data you can:

• Develop accurate models of daily, weekly, monthly, and quarterly loan application volumes, and apply this insight to plan staffing and resources;
• Calculate average times required to handle different steps of the underwriting process, including turnarounds, first decisions, conditional approvals, and declines, and use this information to establish benchmarks to compare productivity throughout the year;
• Determine which of your underwriters are the most/least productive and identify the skills that influence greater productivity; and
• Identify loan application attributes that correlate with difficult loan decisions and employ decision rules to facilitate faster, consistent loan decisions.

Auto lending analytics give you the ability to monitor and evaluate the efficiency of your underwriting process at very granular levels. They also help continually improve process efficiency in response to market dynamics, regulatory changes, and adjustments to credit policies.

Auto Lending Analytics Identify Sources of Risk

In addition to pinpointing underwriting process inefficiency, auto lending analytics can identify potential sources of risk. Exceptions and overrides, when done by experienced underwriters, positively impact performance. However, when done simply for the sake of meeting monthly or quarterly bookings targets, those decisions can lead to delinquencies and defaults.

Auto lending analytic allows you to monitor the number of exceptions and overrides. Comparing these trends monthly and quarterly shows unusual peaks or troughs worthy of further investigation. For example: Is there a correlation between the number of overrides and underwriter experience? What reasons are given for exceptions? Should credit policies be modified? Would decision rules or additional levels of approval help bring more consistency to exceptions and overrides? How have past exceptions impacted portfolio performance? (For a detailed discussion of that topic, please see Why Auto Loan Portfolio Analytics Are So Valuable.) With auto lending analytics, you identify decisions that potentially introduce risk into your portfolio, and using the capabilities of a modern loan origination solution, make changes that minimize, and ideally eliminate, these sources of risk.  

Advantages of Fully-Integrated Auto Lending Analytics

Legacy auto lending solutions present inherent barriers to the efficient use of analytics. Typically, operational data from the lending system is extracted, normalized and then analyzed using Excel or Access applications built and customized by in-house gurus. Problems with this approach include:

• Technical and coding skills (in-house or via consulting services) required to develop these applications;
• Data quality and governance issues. You need a data glossary to determine how data are derived and what they mean for proper interpretation via analytics;
• Delays caused by a manual or inefficient extraction or downloading process prevents quick access of current processes; and
• Detailed knowledge of Excel or Access applications is required to conduct a wide range of analyses.

In contrast, modern auto lending analytics avoids these barriers by providing fully-integrated auto lending analytics that:

• Eliminates the need for technical or coding skills to develop analytic tools.
• Understands your lending data. There’s no need to be concerned with data schemas, quality, or interpretation.
• Provides near real-time reporting to allow business users to monitor the current trends and proactively identify and mitigate inefficiencies and potential risks.
• Allows business users to easily point and click to select, filter, group data, and drill-down to obtain the detailed analyses they need.

Auto lending analytics provided as a key component of a modern loan origination solution offers lenders one of the most effective means to evaluate and optimize the efficiency of underwriting processes. In a hyper-competitive market, lenders need to identify every opportunity to improve underwriting process efficiency.