3 KEY PROBLEMS WITH AUTO LENDING ANALYTICS AND HOW TO AVOID THEM

The auto lending process—applications through final payments—creates volumes of valuable applicant, borrower, process, and portfolio data for lenders. When auto lending analytics are properly applied to that data you can:

• Obtain valuable insight regarding the sources and attributes of applicants;
• Determine if loan decisions actually reflect the credit policies you’ve established;
• Identify bottlenecks in any areas of your lending process; and
• Help assess the overall profitability of your portfolio.

Auto lending analytics can show you developing trends, so that you can seize opportunities when they arise, or make credit policy changes to mitigate problems. Considering the current state of the auto market, auto lending analytics is a requirement for any lender that wants to remain competitive and profitable.

Auto Lending Analytics: Potholes and Roadblocks

Many lenders have a rough road toward auto lending analytics and its measurable benefits, filled with potholes and roadblocks. In our many years of working with auto lenders we’ve seen three key problems to be overcome:

• Lending software is incapable of capturing, creating or managing the needed data, or is limited in its ability to do so
• Lending software doesn’t allow the lender to define or customize the types of applicant, borrower, process, and portfolio data required for their analyses
• Analytics functionality is not fully integrated into the lending system. It may be provided by a third party, or in the worst case, the lender relies upon export of data for analysis in Excel

Knowing about these potential roadblocks in advance can help your organization avoid them.

Absent or Limited Ability to Capture Data

Without the volumes of detailed data that describe, characterize, and measure your business, analytics has no value. You need to be able to capture data throughout your entire lending process, from application submission through servicing. That’s the foundation for good analytics practices.

Auto lenders using legacy lending software are usually limited in the types of data that can be captured and managed. Legacy lending software is based on technology that may be decades (in the worst case) old. It doesn’t easily accommodate current lending practices and ever-changing regulations. Undoubtedly it requires time-consuming and costly customization any time you wish to make a change (assuming that the software will indeed allow changes). Make sure your software meets the needs of today’s lending environment by asking these questions:

• Can you capture and process applications submitted via mobile devices?
• Does it support data verification or access to 3rd party data sources to ensure data fields are entered accurately?
• Are you able to track the time required to process an application with a borderline credit score?
• How easily can you determine the leading adverse action reason for the past quarter?
• What data do you wish you could capture, but can’t?

Legacy data schemas, interfaces, and outdated architectures cannot adequately support the wealth and variety of data now generated by the lending process. These systems were not designed to take advantage of the cloud or mobile devices. Some legacy systems may not be able to handle the volumes of data required for proper analysis. You can’t thrive in the present and future if your technology is based on the past.

Difficulty Defining the Types of Data to Be Captured

Although there is a wealth of potential data generated throughout the modern lending process, each lender needs to define the data to be captured, managed, and analyzed. Market focus, credit policies, and regulations drive that decision. However, data selection is determined by the types and volumes of data your lending system was designed to handle and your ability to make modifications. You can assess your current lending system’s capabilities by asking these questions:

• Can you define new or custom fields and determine where and when they are used in the lending process?
• Does it allow you to create formulas to use as custom data elements throughout the lending process?
• Can you determine whether data fields will be pre-populated based upon existing data or formulas, or entered manually and have rules to verify accuracy?
• Do you capture data, such as auto-declines and adverse action reasons, applicant military status, or number of overrides or exceptions that would be useful in demonstrating regulatory compliance?

Legacy lending systems can be limited. The ability to extend the system’s ability to create new data types or make modifications typically requires expensive custom programming. You need the freedom to define (and customize) the data that accurately describes and measures your lending practices.

Analytics Not Tightly Integrated With Lending Software

For many lending systems analytics is an afterthought, if it’s thought of at all. In those instances, the vendor may recommend a third-party vendor whose software can be used to analyze your lending data. That approach likely requires consulting services and customized integration.

A lender could employ the universal analytical tool—Excel. But even if everyone at your organization is an Excel wizard, once you’ve downloaded data, it’s static. It’s far better to get lending data updates in real time. That lets you monitor trends as they are happening and adjust reports to run “what if” scenarios.

The process requires the knowledge to extract data from the lending system, apply various Excel functions to analyze the data, create the summary reports and graphs, and share the results via email or a common repository. None of these are ideal. The power and benefits of analytics should be readily accessible. Avoid analytics tools that require:

• Extensive or costly integration to access the lending information in your data warehouse
• Data scientist skills in order to understand and utilize its capabilities.
• Custom programming or SQL skills to define the analytics reports you need.

Access to analytics should be as secure and easy as the application, underwriting, funding, and servicing capabilities of your lending system. You’ll want to use analytics regularly, to summarize process metrics, explore portfolio segments, and investigate the details of certain loans when necessary.

How to Avoid Problems with Auto Lending Analytics

Analytics is vital to lending efficiency, competitiveness, and profitability. If any or all of the above accurately describe your current challenges with analytics, you should update your software. Make sure it can:

1. Capture and manage loan and process data;
2. Let you define and customize the types of data you need; and
3. Have fully-integrated analytics straight out of the box.

These capabilities let you explore every facet of your lending business, from applications through servicing. Analytical insights help you improve lending process efficiency and portfolio performance.

defi ANALYTICS Solves 3 Key Problems

defi ANALYTICS is fully integrated with the defi Loan Origination System (LOS) and defi Servicing. In addition to addressing all of the problems described above, it gives your business analysts immediate access to the tools they need (it’s cloud-based), and lets you easily define, build and configure self-service reports for specific business needs – underwriting, funding, and servicing.