The overarching theme of the global consumer lending landscape over the past couple of years is focused mainly on one thing: How should lenders look at setting loan and lease pricing and managing portfolio risk in a market that is constantly changing?
Many lenders around the world look to AI-powered pricing analytics to handle market volatility and respond to competitive shifts. However, using complex data analytics tools may sound overwhelming to many business and pricing managers. This blog post is focused on bridging the gap between data science complexity and everyday business tasks, showing you that there is an easier way to analyze your pricing strategies and make data-driven decisions.
The best analytical pricing tools are made for:
modeling risk and profitability
predicting customer behavior
creating optimized pricing strategies
But analytical tools are usually built for analytical teams. Using such tools can seem complex and intimidating for business users, including pricing managers (who are not professional data scientists) and finance, risk, legal and compliance leadership (who are not the day-to-day users of the platform). A sophisticated pricing analytics platform is underutilized as a result, IT teams and data scientists are called to the rescue more often than they would like to, the pricing cycle is slow, and competitive advantage is lost.
When pricing analytics software is not focused on user-friendliness, we see pricing and business managers struggle mainly with these four challenges.
Challenge #1 - Lack of strategic analysis
Pricing managers often struggle with tracking the performance of their business strategies, making it very hard to determine which strategies are working and which are not. This results in a reactive approach to loan pricing that can lead to loss of market share and volumes.
Challenge # 2 - Lack of simulation capabilities
Taking a guess is not always helpful, even if one knows the industry very well. Pricing managers lacking “what-if” simulation functionalities are unable to determine whether to raise prices or lower prices, and if so - by how much and when, and effectively do not have the ability to control the risk profile of their portfolios, given the recent increases in cost of funds and the trend of rising delinquencies.
Challenge #3 - Internal dependencies
When data and models are not easily accessible, it can be very challenging for pricing managers to make informed decisions. Reaching out to IT teams every time there is a need to update rates or involving data scientist teams for every simulation request is not effective, being time and resource consuming.
Challenge #4 - Time to market
If it takes you 30 working days to create a new pricing strategy and update your rates while your competition can do it in three hours, you have a problem! Creating new pricing strategies from scratch, comparing them, and getting them approved by a pricing committee can be very time consuming, especially when dealing with complex data and dynamic markets.
Too much complexity in the pricing analytics systems always leaves a lot of frustration within pricing teams. While it is important to keep your spread target and cost of funds in mind when pricing and deploying consumer loan rates, it is just as important (if not more) to understand how customers and the overall market would react to pricing adjustments.
Does that all mean that you must choose between analytical sophistication and business-oriented solutions? Definitely not.
Pricing is a complex process. It involves multiple teams across an organization. From years of working with banks and lenders such as yourselves, I learned that the key to success rests on two pillars: Collaboration and Separation.
Collaboration enables all the different teams involved in the pricing process to work within a single solution.
Separation, on the other hand, provides dedicated and separated workflows for each team, focusing on three major areas: analytical modeling, pricing, and decision support. These dedicated workflows are represented by different user interfaces that are built for supporting the specific team or part of the pricing and decisioning process. What brings it all together is a user-friendly solution, powered by advanced AI and machine learning.
How does it all come together?
In the analytical workflow, for example, data scientists and analytical teams can upload new data, build, and manage models and pricing methods, thus creating a strong analytical foundation for pricing decisions. When it comes to strategic analysis and decisioning, pricing and business managers use their own workflow that leverages advanced analytics and easily turn it into business insights.
Pricing and business managers need to be able to easily monitor their pricing strategy performance, identify any gaps or new revenue opportunities and have the ability to make informed decisions by tapping into underlying data and models.
Earnix Pricing Accelerator, our business dedicated workflow, is made for supporting these needs. Business and pricing managers can use visual analysis and KPI reports to get a clear insight into pricing strategy performance, compare strategies and identify opportunities for improvement.
They can run simulations on different pricing scenarios based on predictive analytics, providing a clear understanding of how these strategies are likely to perform. The direct connection to data and analytics makes advanced analytics highly accessible for exploring various pricing strategies and their potential impact on their business without the need for extensive analytical knowledge, and reusable templates and out-of-the-box user flows allow the pricing managers to respond quickly - saving time and reducing errors.
Using the Earnix Pricing Accelerator
Let’s deep dive into the day-to-day process and how it will look like using Pricing Accelerator.
Figure 1
