11.2% higher
NIBT revenue
Increased confidence in forecasting and automated modeling
60% reduction in time
spent on rate changes
CHALLENGE:
Tech Limitations and Wasted Resources
The U.S. auto lender maintained a highly evolved manual system to determine pricing, driven by a skilled and experienced team of pricing analysts. However, the lender discovered that they were required to do a lot of “course correcting” to stay on track to meet volume and profitability goals. The frequently in changing rates was not only time-consuming but wearing down both human and IT resources.
SOLUTION:
Big Data Analytics, Powerful Optimization, Market-leading Competitive Data
In a review of price decision options, the lender ran a test of Nomis Solutions’ price optimization software against its current processes. Incorporating over 15 years of Auto Finance experience, Nomis’ sophisticated technology uses big data analyses to understand customer, dealer and competitor behavior and identify ongoing trends. Its modeling capabilities allow pricing analysts to predict the impact of various options based on the lender’s customized thresholds for credit risk, new versus used, dealer versus direct, risk tolerance, and volume versus profit.
Compared to the control group, Nomis’ technology delivered the following results:
- 11.2% higher net income while maintaining volume targets
- 60% reduction in time and effort spent on pricing workflows
- Forecasting with increased confidence by incorporating transaction-level competitive data via J.D. Power and automated modeling capabilities
SOLUTION:
Ongoing Scalability and ROI
By facilitating a deeper understanding of pricing factors, Nomis’ granular capabilities enabled the auto lender to significantly improve systems, processes, and productivity. Logical software-driven, constraint-based price optimization predicts results, visualizes tradeoffs, and creates high-definition models reflecting the bank’s preferences for profit and growth. An advanced pricing process will quickly evaluate market shifts, adapt to new customer-, competitor-, and industry-specific behaviors, thus unifying pricing data to inform consistent, repeatable, and accurate pricing processes.
Further, Nomis’ innovative software tools free data scientists to focus on strategic tradeoffs instead of number crunching, therefore improving the ability to ensure and demonstrate regulatory compliance, instill confidence in pricing decisions, and preserve proprietary modeling IP.
Combined, these capabilities provide the bank with a stable foundation to further expand and refine its price optimization strategies to reflect regional differences and other dimensions that affect price sensitivity. In addition to the near-immediate results of implementing the software and Nomis’ best-practice consulting, price optimization continues to improve over time.
Models are regularly re-calibrated through continuous machine learning, which increases predictive accuracy and creates a closed-loop pricing process that regularly reviews and iterates pricing scenarios based on past performance and current goals. As a result, the bank will be able to meet changing corporate or market dynamics and realize an ongoing return on investment.