Big data and analytics are transforming every process- and data-intensive industry. Organizations that fail to recognize the value (and the necessity) of employing analytics as part of their business strategy are destined to take a back seat to the competition.
Modern, cloud-based loan origination solutions (LOS) take advantage of analytics to significantly improve lending efficiency. Applicant and operational data obtained and generated during the loan origination process can be analyzed in may ways to provide summary and detailed lending process insight. With integrated analytics, auto LOS achieves a greater level of efficiency to lift profits and portfolio performance.
Integrated Analytics: Auto LOS Applicant Data
Loan application data contains a wealth of valuable lending insight that can be obtained through integrated analytics. By monitoring application attributes over the course of a week, a month, a quarter, and eventually a year, analytics can reveal significant trends, such as:
- Differences in application volumes over time allow lenders to anticipate and allocate appropriate underwriting resources to accommodate changing demands;
- Noticeable decreases in applications from a reliable dealer indicate the need to contact the dealer to determine the reason for the change;
- Changes in the FICO rating distribution of applications may indicate a need to reevaluate credit policies with the goal of fine-tuning risk against portfolio growth; and
- Increases in applications from previously low-volume geographies may be an early indicator of opportunity that deserves sustained marketing focus.
Uncovering the insight inherent in volumes of auto loan applications by using integrated analytics is the first step in improving auto LOS efficiency.
Integrated Analytics: Auto LOS Process Data
A modern auto LOS replaces manual lending processes and decisions with automated workflows and decision rules that significantly improve auto LOS efficiency. Automation also generates detailed operational data that can be analyzed to evaluate the overall lending efficiency as well as reveal opportunities for further process improvement. Integrated analytics applied to auto LOS process data can evaluate the efficiency of key factors that account for lending process efficiency.
- Calculate the average turnaround time for applications based on dealer, FICO score, or underwriter to determine if you are meeting dealer service level agreements (SLA)
- Determine which of your underwriters has the highest efficiency in reviewing applications and loan decisioning. What skills do these underwriters have that could be used to improve the efficiency of newly-hired underwriters?
- Compare exceptions or overrides over the past 6 months. Does any underwriter account for a greater number? What were the characteristics of these deals? Were these sound lending decisions or did they introduce an unacceptable degree of risk?
- Is there any specific step in the underwriting process that seems to require an unusual amount of time, regardless of underwriter, credit score or source? Could decision rules be applied to improve the efficiency of that step?
The wealth of process data is the second step in improving auto LOS efficiency. These data let lenders evaluate auto LOS efficiency, identify areas for improvement, and apply the latest fintech workflow and decision rules capabilities to achieve ever-greater efficiency.
Integrated Analytics: Auto LOS Perspective from a Client
We can cite the benefits of integrated analytics for auto LOS, but the actual experience of a client is more compelling. One national lender recognized the value of analytics early on and developed a home-grown system. Created by in-house Excel and Access power users, data was downloaded from the warehouse and analyzed to provide reports.
Although this seemed to be a straightforward approach to implementing analytics, the solution was fraught with problems. Inconsistency in data quality and data definitions resulted in differing analytical results. A data glossary was necessary to understand how data were derived and what they meant. Downloading and analyzing the data creating the reports was a protracted process. The home-grown solution demanded an increasing amount of IT overhead, and eventually, a decision was made to adopt a modern auto LOS with integrated analytics. A few of the key benefits of this approach include:
- No need to wrestle with data quality and data glossary issues. Integrated analytics understands the data sources and how they are used to conduct the analyses and produce reports;
- Business users can quickly configure analyses and reports via an intuitive “click and select” user interface—no data scientists or gurus required. Reports are easily customized to deliver the information pertinent to departments and individuals;
- Near real-time reporting allows business users up-to-the-minute reports regarding applications, process efficiency, turn-around times, and service level agreements; and
- Ability to determine how often credit policy rules were overridden, and by whom. In the cases of experienced underwriters, override decisions usually resulted in well-performing loans. In the case of less-experienced underwriters, a greater degree of risk was associated with those decisions, indicating the need for training or an additional level of override approval.
Integrated analytics helped this lender identify decisioning bottlenecks, determine the factors that caused these delays, and whenever possible, replace manual decisions with automation using decision rules. For certain tiers of qualified applicants, they were able to increase auto decisioning by 60%, resulting in an increased booking rate.
defi SOLUTIONS‘ loan origination and analytics software experts understand your lending challenges. As one of the Top 50 Most Promising Fintech Providers for 2018, we welcome the opportunity to discuss auto LOS efficiency with you. Take the first step by contacting our team today or registering for a demo of defi LOS.
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