In this series, I discussed how digital transformation is reshaping the origination processes and improving the customer experience. The previous two blogs have looked at how automated systems can be used to drive new business growth, and how it can sustain current customers by improving the customer experience. For the final installment in the series, I want to discuss how analytics can transform the offer determination and decision accuracy process.
Utilizing Analytics to Streamline Offer Determination
Prescriptive analytics can be used to evaluate all possible offer combinations and identify which ones will maximize target performance metrics while adhering to organizational constraints. Doing so can lead to more flexible offers for consumers and increased sales for dealers, without compromising risk or compliance standards.
For example, we all know that a trip to the car dealership can be tedious and anxiety-inducing. However, auto finance providers are looking to make it easier for customers to purchase a new vehicle by leveraging predictive analytics to better understand how specific offer terms will impact uptake, risk and profitability.
Finance providers that do not feel ready to fully automate this alternative deal structure can simply send the offers generated to an underwriter, who can manually select the deals they feel comfortable with. This same capability can be used across the credit lifecycle, notably by helping modify terms with customers who go into arrears. The customer keeps their vehicle, while the lender reduces the number of write-offs and significantly increases the customer’s loyalty to the brand.
Decision Accuracy in Risk Determination
It’s difficult to accurately evaluate credit applications for risk in changing market conditions, and pricing that risk effectively. Risk managers worry about what happens if the economy changes course and cannot react quickly enough – or worse, they know the changes they need to make, but their IT team cannot make any changes to the production system for several months.
As a result, leading banks are utilizing a combination of self-directed analytic techniques that employ the latest machine learning algorithms and structured scorecards to continuously identify better risk segmentation strategies and implement those strategies in a controlled and transparent manner. Banks that deal with high-risk users can leverage prescriptive analytics to evaluate all possible price points and identify which terms will maximize target performance metrics while adhering to organizational constraints.
Origination decisions weigh heavily on future profitability; building analytics into the decisioning process helps to eliminate repetitious, time-intensive and often error-prone manual operations. Automating decisioning can also help banks significantly reduce measurable risk throughout the life of an account and settle the stage for long-term customer relationships, while providing the agility to quickly modify strategies to meet today’s shifting economic conditions.
As the pace of business increases and customer expectations continue to shift, digital transformation will play an increasingly vital role within financial institutions. Whether it’s streamlining the originations process through automation, or improving customer communication with real-time and personalized communications, tomorrow’s success will lie in exceeding customers’ expectations.
Previous blogs:
